Evolutionary programming was proposed more than thirty five years ago for generating artificial intelligence. The original experiments consisted of evolving populations of finite state machines (FSMs) for prediction, identification, and control. Since then, all of the studies with FSMs and evolutionary programming have been limited to the evolution of strictly non-modular FSMs. In this study, a modular FSM architecture is proposed and an evolutionary programming procedure for evolving such structures is presented. Preliminary results indicate that the proposed procedure is indeed capable of successfully evolving modular FSMs and that such modularity can result in a statistically significantly increased rate of optimization.