Many engineering optimization tasks involve finding more than one optimum solution. These problems are considered as Multimodal Function Optimization Problems. Genetic Algorithm can be used to search Multiple optimas, but some special mechanism is required to search all optimum points. Different genetic algorithms are proposed, designed and implemented for the multimodal Function Optimization. In this paper, we proposed an innovative approach for Multimodal Function Optimization. Proposed Genetic algorithm is a Self Adaptive Genetic Algorithm and uses Clustering Algorithm for finding Multiple Optimas. Experiments have been performed on various Multimodal Optimization Functions. The Results has shown that the Proposed Algorithm given better performance on some Multimodal Functions.