TSP is an NP-complete based mathematical problem, which has enormous applications in the field of vehicle routing problems, logistics, planning and scheduling etc. The traveling salesman problem (TSP) is a problem in combinatorial optimization. Several heuristics are there to solve this interesting structure. One of the heuristics, genetic algorithm (GA) is used by many researchers to solve TSP effectively, but they face various problems. GA has so many lacunas, and to overcome these, we have hybridized GA in a novel way. In this paper, we have developed a hybrid genetic algorithm based firefly mating algorithm (HGFMA), which can solve TSP instances with a greater success rate for easy, medium, and hard difficulty level based on number of cities. Our proposed method has controlled “getting stuck in local optima,” considering less population and less generation.