Automation of mining and resource utilization processes on the Moon with teams of autonomous robots holds considerable promise for establishing a lunar base. We present an Artificial Neural Tissue (ANT) architecture as a control system for autonomous multirobot tasks. An Artificial Neural Tissue (ANT) approach requires much less human supervision and pre-programmed human expertise than previous techniques. Only a single global fitness function and a set of allowable basis behaviors need be specified. An evolutionary (Darwinian) selection process is used to train controllers for the task at hand in simulation and is verified on hardware. This process results in the emergence of novel functionality through the task decomposition of mission goals. ANT based controllers are shown to exhibit self-organization, employ stigmergy (communication mediated through the environment) and make use of templates (unlabeled environmental cues). With lunar in-situ resource utilization (ISRU) efforts in mind, ANT controllers have been tested on a multirobot resource gathering task in which teams of robots with no explicit supervision can successfully avoid obstacles, explore terrain, locate resource material and collect it in a designated area by using a light beacon for reference and interpreting unlabeled perimeter markings.