Previous research has used behavior hierarchies to address the problem of coordinating large numbers of behaviors. However, behavior hierarchies scale poorly since they require the state information of low-level behaviors. Abstracting this state information into priorities has recently been introduced to resolve this problem. In this work, we evaluate both the quality of priority-based behavior hierarchies and their ease of development. This is done by using grammatical evolution to learn how to coordinate low-level behaviors to accomplish a task. We show that not only do prioritybased behavior hierarchies perform just as well as standard hierarchies but that they promote faster learning of solutions that are better suited as components in larger hierarchies.