This paper presents an innovative method for optimizing job scheduling in manufacturing cells using the Simulated Annealing approach. Traditional methods like Branch and Bound and Integer Programming often provide optimal solutions, but they can be computationally intensive for large or complex problems. As a solution, heuristic methods are introduced. These methods, while not always optimal, are tailored for specific problems and offer more practical solutions with computational efficiency. In this research, a two-phase analysis of job shop scheduling is conducted with the aim of minimizing the number of open cells in a cellular manufacturing system and ensuring the lowest job completion time using Simulated Annealing. A case study is carried out on the developed model to demonstrate the computational efficiency of the Simulated Annealing approach in maintaining the minimum number of cells and completion times, compared to a mathematical model solution.