The rough idea is this. According to the scaling law whose best known consequence is Kepler's Third Law, we may as well assume that the period is . The orbit obeys the principle of least action, which means that locally along the path the integral of the action A = K.E.  P.E. (the difference between the kinetic and potental energies) is a minimum. This suggests that in order to find a choreography we start with almost any path and try to deform it into one with the least action compatible with its topology. A choreography involving N bodies is determined by a periodic smooth curve in the plane. The components (x(t), y(t)) may therefore be expanded in a Fourier series, the sum of harmonic terms (cos nt, sin nt). We can't work practically with an infinite number of terms, so we work only with truncated Fourier series. This means that we are now working on a space of finite although possibly very large dimension. We try to deform the path so as to minimize the action associated to the choreography, without having the N bodies collide in the motion associated to any one of the deformations. To do this we can apply any one of several possible techniques of numerical minimization.
The action associated to a choreography of N bodies with mass m on a path q(t) of period T is given by

. 
It is not impractical, using the fast Fourier transform, to express and calculate this explicitly in terms of the Fourier coefficients of q, and it is also not much more difficult to express and calculate the gradient of A as a function of these coefficients. So we are reduced to a fairly standard computational problem: given a function A = A(q_{1}, ... , q_{n}) whose gradient we can calculate, how do we find a point where A achieves at least a local minimum? Theoretically, one could follow down the paths of steepest descent on the graph of A. In practice, this leads to disaster. (Christopher Moore suggests that that is what he did in the original discovery of his periodic orbits, but it is hard to believe that he did so.) The differential equations x' = grad(x) one gets will almost certainly be stiff, which means that numerical techniques of solving them are tricky, and to be avoided if possible. There is a well known technique of minimization, however, which can be applied successfully  the conjugate gradient method.
In the following applet, I demonstrate how action minimization works. We are looking for a path f(t) = (x(t), y(t)) along which 3 bodies can run in gravitational equilibrium (i.e. N=3 in the formula for action). It starts off with a curve mildly perturbed from the Lissajous curve (sin t, sin 2t). As it runs, this path is deformed into the figureeight choreography orbit of three bodies first found by Moore. Here, for demonstration purposes, we use only Fourier series of 12 terms. (It is important that the number of bodies divide the number of terms in the Fourier series. Because the center of mass is fixed at the origin, the Fourier coefficients whose index is divisible by 3 vanish.) Along the way, the current action is displayed.
After such a program has been run, you can check whether or not you have found a reasonable candidate for a true system by examining the virtual acceleration along the path. If the orbit is authentic, then Newton's second Law F = m a is valid, where the force F is that exerted by the other bodies. The virtual acceleration of a body at time t is the function F(t)  m a(t), which will vanish only for true orbits.
Action minization is not the only way to compute periodic orbits. Doing it accurately, especially for complicated orbits, requires a very large number of terms in the Fourier series (C. Moore apparently used only 5060, but Simó has worked with more than a thousand). This is especially difficult because in a space of dimension n calculating the gradient is a task of order n^2 in complexity. Another possibility is to start with a point and a velocity, solve the differential equations arising from Newton's laws, and adjust velocity until you have come back to the same starting pooint with the same starting velocity. Simó has in fact done this, with great success. But even a twodimensional `space' is vast when you are contemplating such a task, and a quick and dirty action minimization is required to give you some idea of where to probe.
References
In addition to References:

William H. Press, Brian P. Flannery, Saul A. Teukolsky, and William T. Vetterling, Numerical Recipes  the art of scientific computing, Cambridge University Press, in any of several edition.
This book is well known for the thoroughness of its coverage as well as the sloppiness of its code. Chapter 10, on minimization, is nonetheless one of the best general references for the conjugate gradient method, particularly for the crucial onedimensional minimization routine called repeatedly by the higherdimensional one. Beware: in the edition I have, a sign is wrong in the formula for a parabolic minimum in one dimension, in the text and in the printed code as well.


Jonathan Richard Shewchuk, An introduction to the conjugate method without the agonizing pain, http://www.cs.berkeley.edu/~jrs/, 1994.
These popular notes, as far as I know otherwise unpublished, are largely concerned with using the conjugate gradient method in algorithms of linear algebra. The discussion of how to use it for nonlinear minimization problems is somewhat skimpy  perhaps even slightly misleading  but the copious pictures nonetheless make it a valuable source. It serves well to supplement the somewhat cryptic motivation in Numerical Recipes, which has few (mediocre) pictures.

