In this paper dynamic method for solving travelling salesman problem was suggested. It was shown that initial solution obtained by heuristic algorithm can be improved during it’s execution. The experimental level of dynamic stability for genetic algorithm solving travelling salesman problem was evaluated. This work also includes comparison of two algorithms: classical genetic algorithm (GA) and dynamically advanced genetic algorithm (DAGA). As a result, dynamically advanced genetic algorithm is more useful due to the fact that it generates shorter routes than classical algorithm. DAGA algorithm reduces length of the shortest solution in certain experiment as well as average length of all routes in it.