Material defects govern the performance of a wide range of energy conversion and storage devices, including photovoltaics, thermoelectrics, and batteries. The success of large-scale, cost-effective manufacturing hinges upon rigorous material optimization to mitigate deleterious defects. Material processing simulations have the potential to accelerate novel energy technology development by modeling defect-evolution thermodynamics and kinetics during processing of raw materials into devices. Here, a predictive process optimization framework is presented for rapid material and process development. A solar cell simulation tool that models defect kinetics during processing is coupled with a genetic algorithm to optimize processing conditions in silico. Experimental samples processed according to conditions suggested by the optimization show significant improvements in material performance, indicated by minority carrier lifetime gains, and confirm the simulated directions for process improvement. This material optimization framework demonstrates the potential for process simulation to leverage fundamental defect characterization and high-throughput computing to accelerate the pace of learning in materials processing for energy applications.