Time, quality, and cost are the most critical performance indicators in project management. It has always been considered a tough challenge for project managers to optimize them simultaneously. This paper aims at establishing a simulation-based integer linear programming tool that helps project managers, at the preliminary stages, to assess the risks related to the feasibility and profitability of the projects within the framework of a stochastic discrete time-cost-quality tradeoff problem. The computational experiments on a wide range of benchmark instances from the literature were performed, and the results were compared with those of the deterministic version of the problem. The proposed approach is able to assess the impact of the stochastic behavior of the duration and the quality of the tasks on the cost, duration, and quality of the whole project. Moreover, the simplicity and the reduced time required for the computation of large size networks revealed to be very promising for giving a practical solution for real-life projects. INDEX TERMS Monte-Carlo simulation, integer linear programming, project management, risk.