The poor performance of traditional multi-objective optimization algorithms over the many-objective optimization problems has led to the development of a variety of many-objective optimization algorithms. Recently, several many-objective optimization algorithms have been proposed to address the different class of many-objective optimization problems. Most of the existing many-objective optimization algorithms were designed from the perspective of synthetic many-objective optimization problems. Despite the tremendous work made in the development of the many-objective optimization algorithms for solving the synthetic many-objective optimization problems, still real-world many-objective optimization problems gained little attention. In this work, we propose a grid-based many-objective particle swarm optimization (GrMaPSO) for the many-objective software optimization problem. In this contribution, the grid-based selection strategies along with other supportive strategies such as two-archive storing and crowding distance have been exploited in the framework of particle swarm optimization. The performance of the proposed approach is evaluated and compared to three existing approaches over five problem instances. The results demonstrate that the proposed approach is more effective and has significant advantages over existing many-objective approaches designed for the software module clustering problems.