The molds industry, being a highly competitive sector, is constantly driven by organizations looking to optimize their business processes to gain a competitive edge. To access advanced management tools, there has been a strong push for integrating Industry 4.0 technologies, which in turn has resulted in a significant increase in the amount of manufacturing process data, enabling more accurate and real-time analysis of processes. However, challenges persist due to the unpredictable and ad-hoc nature of this industry’s business processes. As a result, effective resource management has become a crucial aspect that organizations are actively addressing. In this study, our objective is to leverage process mining algorithms and complement them with a genetic algorithm to generate efficient scheduling solutions to produce mold parts on factory floors. By analyzing existing data and identifying the workflows through process mining, we seek to gain a more realistic perspective on the mold manufacturing business process. Through this analysis, our aim is to uncover meaningful workflows and sequences for producing mold parts, ensuring that critical activities are appropriately incorporated while minimizing unnecessary repetitions. By identifying these purposeful sequences, we can provide the factory floor with schedules that optimize resource usage and enhance the overall efficiency of mold manufacturing processes.