We couple MC minimization with simulated annealing to enhance the global-optimization search. Annealing the simulated temperature, T , changes the tolerance of accepting moves over the course of the MC minimization process. At higher temperatures, perturbations which increase the system energy are selected with higher probability, creating a more exploratory search of the potential energy surface. At low temperatures, moves which increase the system energy are accepted with lower probability, causing the MC minimization to get trapped in local minima and exhaustively sample the local potential surface. Combining exploratory and exhaustive sampling ensures solutions consider a wider range of the solution space compared to traditional gradient-descent optimization.