In real world water distribution systems (WDS) uncertainty can arise in a number of different ways. Variations in the performance of parts (for example pipe roughness) can affect the performance of the system. Uncertainty in the requirements the system must satisfy (such as demand at a node) will affect the ability of the system to meet those requirements. An algorithm which can reduce the number of fitness evaluations required to find performance probabilities for systems operating under uncertainty has the potential to significantly reduce computation times required for optimisation. Furthermore when system uncertainties include mechanical failures such as pipe bursts, blockages and leaks, costs can be associated with underperformance allowing such an algorithm to offer risk-based optimisations of systems by assigning an expected consequence of failure to each design. Such optimisations will find a family of solutions offering a trade-off between the cost of the system and the expected future costs or consequences due to failures and other uncertainties.The need for an optimisation technique which is not only capable of optimising systems under uncertainty, but is also scalable to large WDS is at the heart of the proposed research.This research project brings mathematical techniques for statistical sampling and evolutionary optimisation together with an engineering knowledge of the design of water distribution systems under uncertainty.