Traveling salesman problem aims to find the shortest route. A salesman travels to each of the cities once. Genetic Algorithm is used for solving this problem, which returns the best solution found showing the distance that can be covered with the minimum cost (shortest path). A Spark-enabled parallel implementation was investigated in terms of performance. The aim of the study was to show the effect of parallelization of a Genetic Algorithm applied to the Travelling salesman problem. Experiments are run using different numbers of processors and the performance of the algorithm is evaluated based on the execution speed. To identify the best performing number of processors to be used, we made a comparison measuring the execution time of the algorithm for different numbers of cities using different numbers of cores.