In this paper, a new type of vehicle routing problem in the valuable commodity transportation industry is modeled considering the route risk constraint. The proposed model has two objective functions for risk minimization. In the first objective function, three concepts are presented, which are: 1) the vehicle does not travel long distances in the first three moves because it carries more money, 2) to serve the same branch on two consecutive days, at the same time 3) The bow should not be repeated in two consecutive days. This reduces the possibility of determining a fixed pattern for the service and increases the security of the service. In the second objective function, risk is a function of the amount of money, the probability of theft and the probability of its success. To solve the proposed model, two different meta-heuristic algorithms including genetic algorithm and ant colony optimization algorithm have been used. In computational testing, the best parameter settings are determined for each component and the resulting configurations are compared in the best possible settings. The validity of the answers of the algorithms has been investigated by generating different problems in various dimensions and using the real information of Shahr Bank. The results show that the genetic algorithm provides better results compared to the ant colony algorithm with an average of 0.93% and a maximum of 1.87% difference with the optimal solution.