Global urbanization and climate change are intensifying challenges in the sustainable management of the Food–Energy–Water (FEW) system. This study introduces a multi-objective optimization framework that redefines urban spaces through a dual rooftop-ground hierarchy, interlinkage nodes for mapping material and energy flows, and the application of NSGA-II optimization to balance food production, energy output, and costs. The framework was applied to a case study area, generating non-dominated solutions with diverse resource-cost configurations. The findings revealed that optimal scenarios could meet 40.6% of local energy demands and exceed 102.9% of local grain demands, while maintaining economic viability. This approach bridges resource systems theory and spatial planning practice, providing economically viable pathways for high-density cities to transform into hybrid production-consumption spaces, effectively addressing the dual pressures of urbanization and climate change.

High-density urban residential areas represent critical hotspots of resource consumption and environmental degradation. By 2050, 68% of the global population is projected to live in urban areas, driving a 70% increase in food demand and a doubling of energy demand. Although Food-Energy-Water (FEW) system imbalances are increasingly recognized, translating conceptual frameworks into quantifiable, economically viable spatial strategies remains a significant challenge for sustainable development. This study proposes a multi-objective optimization framework to transform FEW system in high-density urban areas. The framework integrates three key innovations: a dual rooftop-ground hierarchy that redefines residential spaces as integrated micro-production units; quantitative interlinkage nodes that map material and energy flows across subsystems; and the NSGA-II to simultaneously optimize food production, energy output, and system costs. This integrated approach overcomes the limitations of prior single-objective models and simplistic scenario analyses, offering a robust method for evaluating spatial trade-offs. The model produced 175 non-dominated solutions, highlighting nonlinear trade-offs between resource outputs and system costs. The cone-shaped Pareto front underscores diminishing marginal returns as resource inputs increase. Sensitivity analysis demonstrated robust model performance, with objective variations constrained to < 3% under ±5% parameter perturbations. The selected compromise solution achieves annual outputs of 5,223 tons of food and 49.09 GWh of energy at a total cost of CNY 160 million, exceeding local demand by 100.2% and 22.6%, respectively. These results significantly enhance local resource self-sufficiency while providing system redundancy to mitigate risks from climate variability and supply disruptions. This study demonstrates that coordinated spatial configurations of FEW system generate synergistic benefits that exceed those of isolated subsystem optimizations. By bridging the gap between resource systems theory and spatial planning practice, the proposed differentiated spatial strategy redefines conventional urban resource layouts, offering a transformative pathway for high-density cities to evolve into hybrid production-consumption spaces. Furthermore, it delivers a scalable and economically viable solution to the global challenges posed by urbanization and climate change.