<p>Several techniques have been proposed to prioritize risks identified in projects. In academia and business, the most standard use is the probability-impact matrix, also known as the risk matrix. Although widespread, this tool has specific limitations, as demonstrated in numerous studies. This article introduces a quantitative method based on Monte Carlo simulation to enhance project risk prioritization. Our proposed method quantifies the impact of each risk on both duration and total cost objectives, enabling us to determine each risk’s relative importance based on these values. In contrast to previous work, this paper proposes a methodology that integrates the effects of risk interactions and the structure of the project-defining network. We conducted two simulation studies, which are detailed in this article. The first study uses fictitious projects to examine the influence of project network structure on the effectiveness of the risk matrix, compared with our proposed methodology. In the second study, we apply our method to two distinct real-world projects and compare these findings with results from simulations of fictitious projects. The conclusions indicate that the traditional risk matrix method produces results that diverge from those generated by our proposed approach, which quantifies impacts. Moreover, it was observed that the risk matrix provides more reliable results when a project’s structure approximates a serial configuration. However, its accuracy in risk prioritization declines for projects with a more parallel structure.</p>