Subject of this thesis is „Path finding optimization in a dynamic environment“. The problem is presented in a traffic network environment with vehicles as object which are moving on said network. Traffic network is modeled with a graph and traffic jams are represented by capacities as weights on the edges of the graph. The chosen approach can be described with two components. The first component is A* algorithm that is used to find the shortest path, considering only space distance between the starting and the ending point. The second part is an ant algorithm which further optimizes given path. The path that is calculated with A* is used to set the initial pheromone trace where the closer the vertex is to the goal the higher the pheromone value will be. This achieved so called zipper effect, where two or more lanes merge at a later point, improving traffic flow. Results achieved were expected. Solutions calculated by ant algorithm were better then those calculated by only A* algorithm. There was some bias towards the shortest path on smaller graph which caused somewhat worse solutions, but on much bigger graphs that same bias helped to speed up ant algorithm search time. With further improving the optimization algorithm, negative effect on smaller graph was reduced to dismissably small levels. Such results have been shown to be satisfactory in the end and there was a significant improvement on the greedy algorithm where solutions were based only on the shortest space distance.