This　study　introduces　a　spatial　layout　framework　for　the　multi-objective　optimization　of　low-impact　development　(LID)　measures　at　an　urban　watershed　scale,　targeting　the　mitigation　of　urban　flooding　and　water　pollution　exacerbated　by　urbanization.　The　framework,　tailored　for　the　Dahongmen　area　within　Beijing＇s　Liangshui　River　Watershed,　integrates　the　storm　water　management　model　(SWMM)　with　the　nondominated　sorting　genetic　algorithm　II　(NSGA-II).　It　optimizes　LID　deployment　by　balancing　annual　costs,　volume　capture　ratio　of　rainfall,　runoff　pollution　control　rate,　and　the　reduction　in　heat　island　potential　(HIPR).　High-resolution　comprehensive　runoff　and　land　use　data　calibrate　the　model,　ensuring　the　realism　of　the　optimization　approach.　The　selection　of　optimal　solutions　from　the　Pareto　front　is　guided　by　weights　determined　through　both　the　entropy　weight　method　and　subjective　weight　method,　employing　the　TOPSIS　method.　The　research　highlights　the　positive,　nonlinear　correlation　between　cost　and　environmental　benefits,　particularly　in　reducing　heat　island　effects,　offering　vital　decision-making　insights.　It　also　identifies　a　critical　weight　range　in　specific　decision-making　scenarios,　providing　a　scientific　basis　for　rational　weight　assignment　in　practical　engineering.　This　study　exemplifies　the　benefits　of　comprehensive　multi-objective　optimization,　with　expectations　of　markedly　improving　the　efficacy　of　large-scale　LID　implementations.