Both energy and water are invaluable intertwined natural resources required for the built environment. Intrinsically, the energy and water sectors are linked for attending to several consumer needs. In local sites such as urban precincts, only a few relevant studies have considered both energy and water for integrated resource planning and management. Early on, unifying the various urban planning facets was reported as an eminent research and development need. Despite the push for greater systemic urban planning, energy-water nexus investigations are still rarely conducted for new and retrofit built environment projects. Reasoning behind the lackluster practice of integrated energy-water planning is the dearth of a general formulation of optimisation models that can capture spatiotemporal energy-water interactions. Lacking cross-sector coordination, traditional energy and water planning for new building and retrofit projects are completed almost always separately at a building and/or precinct scale. To address this singular lensed focus, an original framework was proposed nesting long-term capital and short-term managerial decisions for integrated precinct-scale energy-water system planning. This research aimed to enhance overall energy-water system performance, thereby achieving sustainable urban precinct-scale infrastructure development. This interdisciplinary PhD project explored the ubiquitous applicability of mathematical optimisation besides disciplined convex programming to tackle critical energy-water sizing/scheduling tasks at the precinct scale. Presented throughout this doctoral thesis, research activities were undertaken advancing state-of-the-art applications to optimise integrated energy-water system plans that best utilise invaluable intertwined natural resources in urban precincts. As a central part of the proposed framework, an employable adaptive nested robust multi-objective optimisation approach for integrated precinct-scale energy-water system planning was developed to support decision-making under uncertainty. [...]