Multi-stage Monte Carlo optimization (MSMCO) and its attendant limit theory are applied to the problem of finding the shortest routes to connect 249 points. Comparisons of the MSMCO technique's performance are made with other methods. The limit theory idea of averaging errors or pinning down frequently occurring sub-routes is used extensively. Multi-stage is a series of consecutive regular Monte Carlo optimizations run over an ever changing and narrowing feasible solution region following the best answer so far. The preliminary sampling in the early stages, points the way to the optimal solution region of the sampling distribution of feasible solutions. Then the n-dimensional rectangles (for a function of n variables) focus in and find the oplimals. Multi-stage is of course an approximation technique. However the approximations can outperform many other algorithms if the sampling and limit theory are carefully applied. It also allows one to make use of simplifying transformations (like the ranking transformation used here) to reduce the number of variables in the simulation.