We present the results of evolving articulated virtual creature foraging in a 3D physically simulated environment filled with stationary food objects. Simple block creatures with sigmoidal neural networks are evolved through a genetic algorithm using a fitness function based on the consumption amount. The results show the evolution of successful foraging behaviors performing well in environments with various food distributions. We analyze the foraging based on its efficiency, creature morphologies, movement strategies, and the food density and entropy in the simulation environment.