In this paper, we propose a hierarchical neuroevolution technique for physics-based character animation control. The artificial neural network that makes up the controller is composed of a number of interdependent control modules. As a proof-of-concept, modules for posing, standing, and reaching motions are demonstrated. We show that evolving these modules one-by-one, with each of them dependent on its predecessors, allows evolution to converge faster, and possibly deliver better and more stable results than common, i.e. non-hierarchical, controllers.