One of the critical public safety roles for water distribution systems (WDS) is suppression of urban fire events. Previous studies have investigated WDS rehabilitation with a major focus on improving reliability by pipe enlargement. However, pipe enlargement can cause water quality problems and place public health at risk during normal operational periods. Thus, a novel approach is required to effectively address the conflicting goals of the WDS: reliable delivery of water during normal and emergency conditions, meeting water quality standards, and finding cost-effective design and rehabilitation options. In this study an evolutionary computation-based multiobjective optimization-simulation framework is developed to design effective mitigation strategies for urban fire events for water distribution systems with three objectives: (1) minimizing potential fire damages, (2) minimizing water quality deficiencies, and (3) minimizing the cost of mitigation. An elitist nondominated sorting genetic algorithm (NSGA-II) is modified for an evolution strategy (ES)-based implementation to address difficulties for heuristic algorithms posed by WDS problems. Implementation of this methodology generates Pareto-optimal solution surfaces that express the trade-off relationship between fire flow, water quality, and mitigation cost objectives. The method provides decision-makers with the flexibility to choose a mitigation plan for urban fire events best suited for their circumstances. Each Pareto-optimal solution comprises a set of pipes to be enlarged to achieve increased fire flow and the corresponding diameters of these pipes. The algorithm is illustrated with several non WDS test functions. The Micropolis virtual city is then used to demonstrate the application of the proposed methodology to a complex WDS.