The aim of this work is to show that evolutionary computation techniques (genetic programming in this case) can be used to evolve coordination in real-time strategy games. An abstract real-time strategy game is used for our experiments, similar to a board game but with many of the properties that define real-time strategy games. We develop an automated player that uses a progressive refinement planning technique when determining its next immediate turn in our abstract real-time strategy game. We describe two types of coordination which we believe are important in the game and then define measurements for both. We perform twenty coevolutionary runs for our automated player and then analyze the history of each run with respect to the success of the solutions found and their level of coordination. We wish to show that as the evolutionary process progresses both the quality and the level of coordination in the solutions found increases.