This work details the design and simulation results of a bioinspired minimalist algorithm based on C. elegans, using autonomous agents to forage for attractant energy sources. The robotic agents are energy-constrained and depend on the energy they forage to recharge their batteries, which is significant as the foraging task is one of the canonical testbeds for cooperative robotics. The algorithm consists of 6 input parameters which were simulated and optimised in 9 unbounded environments of varying difficulty levels, containing attractant sources which robots would then have to forage from to maintain energy levels and survive the entirety of the simulation. The robots running the algorithm were then optimised using Evolutionary Algorithms and the best solutions in all 9 environments were categorised with the use of clustering techniques. The clustering results highlighted the different strategies which emerged. Ultimately across the 9 environments, 6 different strategies have been identified. The results demonstrate the applicability of the proposed algorithm to localise attractant sources and harvest energy in different scenarios using the same core algorithm.