Local interactions drive emergent collective behavior, which pervades biological and social complex systems. These behaviors are scalable and robust, motivating biomimicry: engineering nature-inspired distributed systems. But uncovering the interactions that produce a desired behavior remains a core challenge. In this thesis, I present EvoSOPS, an evolutionary framework that searches landscapes of stochastic distributed algorithms for those that achieve a mathematically specified target behavior. These algorithms govern self-organizing particle systems (SOPS) comprising individuals with strictly local sensing and movement and no persistent memory. For aggregation, phototaxing, and separation behaviors, EvoSOPS discovers algorithms that achieve 4.2–15.3% higher fitness than those from the existing “stochastic approach to SOPS” based on mathematical theory from statistical physics. EvoSOPS is also flexibly applied to new behaviors such as object coating where the stochastic approach would require bespoke, extensive analysis. Across repeated runs, EvoSOPS explores distinct regions of genome space to produce genetically diverse solutions. Finally, I provide insights into the best-fitness genomes for object coating, demonstrating how EvoSOPS can bootstrap future theoretical investigations into SOPS algorithms for challenging new behaviors.