<p>The accidental release of dangerous substances from industrial sources into the environment is a recurring problem. Despite the regulations implemented to prevent these accidents and mitigate their consequences, managing operations in the aftermath of industrial disasters remains a complex challenge. This paper investigates post-catastrophe operations in the event of industrial disasters, where a group of specialized teams are deployed to clean up areas contaminated by dangerous substances, with the objective of minimizing the overall risk of the operation. To this end, we propose a new optimization problem named Resource Constrained Project Scheduling Problem with Risk and Priorities (RCPSP-RP), which is composed of an integrated scheduling-routing model with different schemes of task prioritization. Two integer linear models and an Iterated Local Search (ILS) metaheuristic are proposed and applied to solve several theoretical instances. A first contribution of this study is the introduction of a new problem that is highly relevant in the context of industrial disasters. In addition, several experiments have been conducted that reveal that relaxation of priority constraints generally leads to solutions with a lower overall risk. Furthermore, the results highlight the limitations of the mathematical formulation, which can only solve instances with up to 16 tasks. In contrast, the ILS approach consistently provides solutions in seconds for all instances.</p>