In today's competitive industry, scheduling plays a significant role in improving the efficiency of manufacturing systems. Hence, many scholars and practitioners have been researching to enhance the quality of scheduling methods. In this research, the focus is on solving a real-world scheduling problem in the food industry which was previously dealt with a very time-consuming manual method without high-quality solutions. The problem is to find the best schedule for producing multiple products on multiple machines in a semi-continuous manufacturing system. Having a continuous section in the system makes scheduling too complicated than the manual method could deal with properly. So, similar to many scheduling problems, in this thesis, metaheuristics (GA and MPSA) are applied to the problem in order to address the defects of the manual method. Selected methods show promising results and performance against the manual method used before. Statistical analysis shows better performance of the genetic algorithm while the other method is more robust to the selected parameters.