To ensure that an organization can schedule projects according to the stipulated time, production scheduling is crucial. Consumer needs must be met on time by the company. If the company fails to deliver on time, it will be penalized or charged late fees, which can be avoided through proper scheduling. Scheduling involves planning and organizing the production schedule to optimize resource utilization and meet customer demand. One way to achieve optimal scheduling is by minimizing the makespan. PT XYZ is a heavy steel fabrication company with a flow shop production process. This research was conducted by applying the Campbell Dudek Smith (CDS) method and the Cross Entropy Genetic Algorithm (CEGA). The objective of this study is to determine the task order and calculate the shortest makespan value based on a comparison of the company’s current conditions and scheduling using the Campbell Dudek Smith (CDS) technique and the Cross Entropy Genetic Algorithm (CEGA) with Python programming. The results showed that the company’s current state yielded a job order of 1-2-3-4-5 with a makespan value of 23,520 min. The Campbell Dudek Smith (CDS) method yielded a job order of 5-3-4-1-2 with a makespan value of 23,520 min, and the Cross Entropy Genetic Algorithm (CEGA) method yielded a job order of 3-4-1-2-5 with a makespan value of 22,080 min. As a result, the most recommended method is to use the CEGA method, which reduces the makespan value by 1,440 min compared to the current condition.