In　recent　years,　the　urgent　need　to　mitigate　stormwater　runoff　and　address　urban　waterlogging　has　garnered　significant　attention.　Low　Impact　Development　(LID)　has　emerged　as　a　promising　strategy　for　managing　urban　runoff　sustainably.　However,　the　vast　array　of　potential　LID　layout　combinations　presents　challenges　in　quantifying　their　effectiveness　and　often　results　in　high　construction　costs.　To　address　these　issues,　this　study　proposes　a　simulation-optimization　framework　that　integrates　the　Storm　Water　Management　Model　(SWMM)　with　advanced　optimization　techniques　to　minimize　both　runoff　volume　and　costs.　The　framework　incorporates　random　variations　in　rainfall　intensity　within　the　basin,　ensuring　robustness　under　diverse　climatic　conditions.　By　leveraging　a　multi-objective　scatter　search　algorithm,　this　research　optimizes　LID　layouts　to　achieve　effective　stormwater　management.　The　algorithm　is　further　enhanced　by　two　local　search　techniques-namely,　the　＇cost-benefit＇　local　search　and　path-relinking　local　search-which　significantly　improve　computational　efficiency.　Comparative　analysis　reveals　that　the　proposed　algorithm　outperforms　the　widely　used　NSGA-II　(Non-dominated　Sorting　Genetic　Algorithm　II),　reducing　computation　time　by　an　average　of　8.89%,　16.98%,　1.72%,　3.85%,　and　1.23%　across　various　scenarios.　The　results　demonstrate　the　method＇s　effectiveness　in　achieving　optimal　LID　configurations　under　variable　rainfall　intensities,　highlighting　its　practical　applicability　for　urban　flood　management.　This　research　contributes　to　advancing　urban　sponge　city　initiatives　by　providing　a　scalable,　efficient,　and　scientifically　grounded　solution　for　sustainable　urban　water　management.　The　proposed　framework　is　expected　to　support　decision-makers　in　designing　cost-effective　and　resilient　stormwater　management　systems,　paving　the　way　for　more　sustainable　urban　development.