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The Vehicle Routing Problem (VRP) has been widely studied throughout its history as a way of optimizing routes by minimizing distances, and the issue of risk in VRP has been received less attention, which is essential to increase transport safety, to reduce accident costs and to improve delivery reliability. In this way, this paper aims to support decision makers to plan routes for a road freight company considering both, i.e. logistics cost and safety. An analytical approach based on statistics was developed in which official government data of accidents occurrences and data from cargo insurance companies were used to estimate the risk cost of routes using the Monte Carlo simulation. The Capacitated Vehicle Routing Problem (CVRP) was employed and logistics and risk costs were minimized by varying a specific safety level coefficient and the model generated solutions with safer routes, reducing risk cost by up to approximately 18%. The accidents probabilities and risk costs of each route represented values as expected and the main contributions of this paper is the applicability of the approach to support decision markers to choose routes considering safety and logistics costs, and to be a simple and adaptable methodology for any VRP model. In addition Knime Analytics Platform was also used to estimate the accidents probabilities and to simplify data exploration, analysis, visualization and interpretation.