Engineering project managers often face a challenge to allocate tight resources for managing interdependent risks. In this paper a quantitative framework of analysis for supporting decision-making in project risk response planning is developed and studied. The design structure matrix representation is used to capture risk interactions and build a risk propagation model for predicting the global mitigation effects of risk response actions. For exemplification, a genetic algorithm is used as tool for choosing response actions and allocating budget reserves. An application to a real transportation construction project is also presented. Comparison with a Sequential Forward Selection greedy algorithm shows the superiority of the genetic algorithm search for optimal solutions, and its flexibility for balancing mitigation effects and required budget.