AbstractThe design of an urban water distribution system (WDS) is a challenging problem involving multiple objectives. The goal of robust multi‐objective optimization for WDS design is to find the set of solutions which embodies an acceptable trade‐off between system cost and reliability, so that the ideal solution may be selected for a given budget. In addition to satisfying consumer needs, a system must be built to accommodate multiple demand loading conditions, withstand component failures and allow surplus capacity for growth. In a developmental setting, WDS robustness becomes even more crucial, owing to the limited availability of resources, especially for maintenance. Recent optimization studies have achieved success using multi‐objective evolutionary algorithms, such as the Non‐dominated Sorting Genetic Algorithm II (NSGA‐II). However, the multi‐objective design of a large WDS within a reasonable timeframe remains a formidable problem, owing to the extremely high computational complexity of the problem. In this paper, a meta‐algorithm called AMALGAM is applied for the first time to WDS design. AMALGAM uses multiple metaheuristics simultaneously in an attempt to improve optimization performance. Additionally, a Jumping‐gene Genetic Algorithm (NSGA‐II‐JG) is also applied for the first time to WDS design. These two algorithms were tested against some other metaheuristics (including NSGA‐II and a new greedy algorithm) with respect to a number of benchmark systems documented in the literature, and AMALGAM demonstrated the best performance overall, while NSGA‐II‐JG fared worse than the ordinary NSGA‐II. Large cost savings and reliability improvements are demonstrated for a real WDS developmental case study in South Africa.