In this paper we examine the build order problem in real-time strategy (RTS) games in which the objective is to optimize execution of a strategy by scheduling actions with respect to a set of subgoals. We model the build order problem as a multi-objective problem (MOP), and solutions are generated utilizing a multi-objective evolutionary algorithm (MOEA). A three dimensional solution space is presented providing a depiction of a Pareto front for the build order MOP. Results of the online strategic planning tool are provided which demonstrate that our planner out-performs an expert scripted player. This is demonstrated for an AI agent in the Spring Engine Balanced Annihilation RTS game.