This paper represents the applications of Genetic Algorithm (GA) to solve a Travelling Salesman problem (TSP). TSP is a simple to describe and mathematically well characterized problem but it is quite difficult to solve. This is a NP-hard type problem i.e. this problem is hard as the hardest problem in NP-complete space. We present the Crossover and Mutation operators, sorting of the solutions to calculate the best optimal solutions. Previously, a numerical illustration was used to signify the model with the techniques. This paper employs Reinforcement Learning to solve the Traveling Salesman problem in the mean of Genetic Algorithm. The technique proposes a model (actions, states, reinforcements).