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Vehicle routing problem finds routes to serve a set of customers. It belongs to the field of intelligent transport systems and logistics. Significant savings can be achieved in real-world scenarios. The mathematical interpretation of the vehicle routing problem is an NP-hard optimization problem. Due to the computational complexity, various heuristics are used to solve the problem within a reasonable processing time. Previous research had been focused mostly on static variants, with constant edge weights represented by expected speed, which results in a too rough approximation of a dynamic traffic environment. The proposed research will take into account the time dependent aspects of the traffic environment. Edge weights will be time dependent functions acquired by analysis of historic GPS paths of vehicles. The proposed method will solve two complex problems: finding a time dependent shortest path in a graph, and solving the time dependent vehicle routing problem.