This paper discusses approaches to cooperative coevolution of form and function for autonomous vehicles, specifically evolving morphology and control for an autonomous micro air vehicle (MAV). The evolution of a sensor suite with minimal size, weight, and power requirements, and reactive strategies for collision-free navigation for the simulated MAV is described. Results are presented for several different coevolutionary approaches to evolution of form and function (single- and multiple-species models) and for two different control architectures (a rulebase controller based on the SAMUEL learning system and a neural network controller implemented and evolved using ECkit).