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This paper describes a genetic algorithm that was developed for optimizing plans in a robotic competition. The algorithm was used both as a static planner, making plans before matches, and as a dynamic replanner during matches, a task with much stricter demands of efficiency. The genetic algorithm was hybridized with a local search technique, which experiments proved essential to finding good solutions in this complex task. To enable rapid response under environmental changes, a heuristic for immediate response and a contingency planning module were also implemented. Experiments proved that the algorithm was able to generate good plans, and continuously modify them in light of a rapidly changing environment.