The Job Shop Scheduling Problem (JSSP) has become one of the key issues in a contemporary manufacturing system in which the task is to optimally schedule jobs to the machines to reduce the time and resources used in production. Good scheduling is critical in enhancing the productivity and competitiveness of manufacturing industries. In this research, Artificial Fish Swarm Optimization (AFSO) algorithm is used to optimize the JSSP in minimizing makespan, total work load and maximum work load in machines. The AFSO strategy models the swarm behavior of fish to search and forage the search space in an efficient manner to prevent its early convergence to local optima. The model incorporates a perturbed global state to improve the direction in search and the speed of convergence. The effectiveness of the suggested AFSO method is compared and tested with the traditional and sophisticated optimization algorithms like Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) methods. The experimental findings prove that the proposed technique provides better results in convergence rate and solution quality. The results prove that AFSO is a useful and promising method of solving complex problems in production system scheduling.