In this paper, we present a variant of the vehicle routing problem (VRP) to increase security in the cash-in-transit sector. A specific index is used for quantifying the exposure of a vehicle to the risk of being robbed along its route. In addition, the problem is subject to a traditional capacity constraint, which limits the maximum amount of valuables that can be transported inside the vehicle. This constraint might be imposed, for example, by insurance companies. A biobjective formulation, aimed at reducing both risk and travel costs, is proposed. The objectives are conflicting since higher risk exposures would allow a reduction of the travel costs incurred by a transportation firm to visit all its customers for the collection of cash. A mathematical model of the problem is described and solved using a progressive multiobjective metaheuristic. Multiobjective optimization and multicriteria decision making are integrated into a single metaheuristic algorithm. To simplify the decision process, the decision maker's preferences are embedded into the multiobjective algorithm. Realistic instances are generated considering the geographical coordinates of several customers (e.g., stores, banks, shopping centers) located in Belgium. The proposed solution approach is tuned and tested both on these realistic instances and standard benchmark instances for the capacitated VRP.