Traffic congestion is a major problem in many urban areas, and has a significant effect on gas consumption and carbon dioxide emissions throughout the world. In the case of a natural disaster, traffic becomes an even bigger problem. This article is focused on strategies for minimizing jam-packed traffic conditions in times of crisis with a Genetic Algorithm that models network-wide optimization decisions. Through the use of the Markov chain theory, researchers are able to extrapolate patterns and other roadway information pertinent to the optimization. The proposed approach also takes into account the need for timely and abrupt action in case of a natural disaster.