We consider the problem of strategic adversarial planning in a Real-Time Strategy (RTS) game. Strategic adversarial planning is the generation of a network of high-level tasks to satisfy goals while anticipating an adversary's actions. In this thesis we describe an abstract state and action space used for planning in an RTS game, an algorithm for generating strategic plans, and a modular architecture for controllers that generate and execute plans. We describe in detail planners that evaluate plans by simulation and select a plan by Game Theoretic criteria. We describe the details of a low-level module of the hierarchy, the combat module. We examine a theoretical performance guarantee for policy switching in Markov Games, and show that policy switching agents can underperform fixed strategy agents. Finally, we present results for strategy switching planners playing against single strategy planners and the game engine's scripted player. The results show that our strategy switching planners outperform single strategy planners in simulation and outperform the game engine's scripted AI.