|Abstract||Protecting critical infrastructure, especially in a complex urban area or region, should focus on identifying and prioritizing potential failure points that would have the most severe consequences. Such prioritization can inform targeted planning and investment decisions, such as what infrastructure should be hardened or relocated first or what infrastructure should receive priority restoration following a disaster, among other uses. Without a prioritization process, assessment and protection programs are typically guided by intuition or expert judgement, and they often do not consider system-level resilience. While understanding how to prioritize high-consequence failure points for assessments and, ultimately, for protection is essential, the complexity of infrastructure systems can quickly overwhelm. For example, in a notional region with 1,000 electric power assets, almost one million failure scenarios are associated with an N-2 contingency and nearly one billion failure scenarios are associated with an N-3 contingency. As a result, it is simply not feasible both technically and financially for system operators and government agencies to assess and prepare for all possible disruptions. Therefore, a primary goal of critical infrastructure protection and resilience programs should be to identify and prioritize the most critical contingencies affecting infrastructure systems. Achieving this goal will allow decision makers to identify high-impact isolated failures, as well as cascading events, and to prioritize protection investments and restoration planning accordingly. To solve this problem, Argonne National Laboratory developed an optimization framework capable of modeling and prioritizing high-consequence failure points across critical infrastructure systems. The optimization framework can model at the system level or the interdependent “system-of-systems” level and is applicable to any infrastructure.