Genetic Algorithms are adaptive heuristic search algorithms which tends to mimic the evolutionary concept of natural selection and genetics. Based on Charles Darwin’s evolutionary principals of “survival of the fittest”, GA’s use techniques such as Selection, Crossover and Mutation that are inspired by natural evolution. Genetic Algorithms express an insightful utilization of a random search, driven to solve optimization problems. GA’s have been used effectively in solving a variety of problems like Telecommunication Routing, Robotics, Computer Gaming, and Trip/Traffic Routing. Travelling salesman Problem is one such problem. Travelling Salesman Problem is such that, given a collection of cities and the cost to travel between each pair of cities, what would be the shortest route to visit all of the cities and return to the starting city. In the typical form, the travel costs are equal such that traveling from city A to city B costs just the same as it would to travel from City B to City A. The travelling salesman problem is known to be a NP-Hard Problem, The purpose of this study is to explore the various solutions proposed to handle the well-known NP-Complete problem of the Travelling Salesman Problem using a genetic algorithm and selecting one of them to implement and validate the results proposed in the paper. After briefly exploring various papers, the paper “Enhanced Travelling Salesman Problem Solving by Genetic Algorithm Technique (TSPGA)” by Buthainah Fahran Al- Dulaimi and Hamza A. Ali was selected. The idea behind the study was to implement the paper and validate the results to check how close they are to the initial findings reported. The further enhancement includes the study of the paper "Hybrid Genetic Algorithm for Travelling Salesman Problem with sorted population". The approach mentioned in this paper has been implemented over the previous papers implementation and the findings are compared.