To keep improving the efficiency-to-cost ratio of photovoltaic solar cells, manufacturing lines must be continuously improved. Efficiency optimization is usually performed process-wise and can be slow and time-consuming. In this study, we propose a machine-learning-based method to perform simultaneous multiprocess optimization. Using the natural variation of a production line, we train machine learning models to investigate the relationship between process parameters and cell efficiency. We employ genetic algorithms to identify new process parameters in order to maximize cell efficiency. The proposed method is demonstrated on a simulated production line of monocrystalline aluminum-back surface field solar cells. Using neural networks, an accurate model is built to predict cell efficiencies from input process parameters with errors of <0.03% absolute efficiency. In five iterations, the mean cell efficiency increases from 18.07% to 19.45%. Provided strong process monitoring and accurate wafer tracking, the proposed method is directly applicable to production-type datasets, enabling the photovoltaic industry to build smart factories and join the fourth industrial revolution.