Criticality is thought to be crucial for complex systems to adapt, at the boundary between regimes with different dynamics, where the system may transition from one phase to another. Numerous systems, from sandpiles to gene regulatory networks, to swarms and human brains, seem to work towards preserving a precarious balance right at their critical point. Understanding criticality therefore seems strongly related to a broad, fundamental theory for the physics of life as it could be, which still lacks a clear description of how it can arise and maintain itself in complex systems. In order to investigate this crucial question, we combine critical learning with evolutionary simulation for a population of Ising-embodied neural networks, striving to find resources distributed over a 2D environment. The results show compelling dynamics in the combination of critical learning with evolutionary computation, highlighting the exploratory nature of critical systems and the pragmatism of evolutionary algorithms. We also analyze the genotypic exploration strategy, exhibiting a tension between local and global scale adaptation.