Landscape hydrological infrastructure (LHI), encompassing both water bodies and green spaces, serves as a crucial component of ecological and landscape infrastructure in urban areas, offering an effective and sustainable solution for urban stormwater management. Achieving an optimal multiobjective LHI planning layout that balances ecological, hydrological, and socio-economic goals remains a critical issue demanding immediate attention. This study proposes a framework for optimizing LHI spatial layout that integrates hydrological modelling with multi-objective optimization algorithms. Implemented on the MATLAB platform, the framework integrates a Cellular Automata (CA)-based hydrological model with a Multi-Objective Optimization (MOO) algorithm, enabling a comprehensive workflow from hydrological simulation to multi-objective evaluation and, ultimately, LHI spatial layout optimization. Using the Heping River catchment in Nanjing as a case study, this research evaluates the application process of the proposed method, emphasizing its strengths and limitations. The results demonstrate that integrating the CA hydrological model with the MOO algorithm enables systematic optimization of LHI spatial layouts, transitioning from quantitative analysis to precise spatial allocation. This approach provides valuable methodological insights and technical support for future multi-objective green infrastructure planning and blue-green space layout, offering a robust foundation for advancing sustainable urban development.