In various real-time situations, the Mobile Robot Path Planning Problem (MRPPP) is one of the most prevalent study fields. In this article, a hybrid path planning technique for mobile robots is used and tested in various environment settings. This approach adopts pre-improvisation and post-improvisation methods for achieving the optimized path with quality with less computational cost. An MRPPP is resolved using a hybrid of the Artificial Potential Field (APF) method and the Multi-Objective Genetic Algorithm (MOGA). The suggested hybrid technique divides the implementation process into three stages. An Artificial Potential Field (APF) algorithm is used to find all feasible paths between the start and destination locations in an environment in order to construct the initial population. By calculating the artificial field produced by obstacles and the target, collision-free paths are created. An optimal solution path is extracted from the initial population of candidate paths using the population-based evolutionary method. In this article, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to identify the optimal solution by concurrently maximizing all the objectives. In the end, the Three Phase Path Refinement Technique(TPRT) is employed to smoothen the derived optimal path. The suggested approach is employed in several maps and the results are compared with other path-planning algorithms to demonstrate the effectiveness of the system.