This thesis deals with the optimization of heterogeneous cargo transport between two locations with a group of vehicles. With the increasing size the problem becomes too complex to be solved with deterministic algorithms. Therefore we designed an evolutionary algorithm which belongs to the family of stochastic algorithms. This kind of algorithms is used for solving problems whose solving time with deterministic algorithms would be unacceptable. A disadvantage of stochastic algorithms is that they frequently do not find the optimal solution. Usually they find a suboptimal solution which is a local optimum of the evaluation function. In this thesis we describe the optimization of heterogeneous cargo transport between two locations with a group of vehicles. We begin by formally defining the problem in terms of cargo and vehicle characteristics. Then we present the evolutionary algorithm characteristics and their examples: genetic algorithms, evolutionary strategies, evolutionary programming, genetic programming and differential evolution. Next we describe the evolutionary algorithm implemented to solve the presented problem which was tested with the predefined parameter values on four test problems. The results were compared with those of the greedy algorithm. The evolutionary algorithm mostly found better results than the greedy algorithm. The algorithm parameters were tuned with a metaevolutionary algorithm which is also described. At the end we present the results obtained with the metaevolutionary algorithm, their comparison with the results of the greedy algorithm and the future work.