With　the　rapid　development　of　urbanization　and　the　continuous　growth　of　population,　the　design　and　planning　of　highrise　residential　buildings　have　become　increasingly　important.　The　purpose　of　this　study　is　to　explore　the　space　optimization　design　method　of　high-rise　residential　buildings　based　on　genetic　algorithm(GA),　focusing　on　the　comparative　analysis　between　traditional　GA　and　Adaptive　genetic　algorithm(AGA).　In　this　paper,　AGA　is　used　to　establish　the　spatial　optimization　model　of　high-rise　residential　buildings.　By　dynamically　adjusting　the　parameters　of　the　algorithm,　AGA　makes　the　algorithm　better　adapt　to　the　characteristics　of　the　problem　and　improves　the　search　efficiency.　The　results　show　that　AGA　is　superior　to　traditional　GA　in　global　convergence　probability,　especially　when　the　population　size　is　large.　AGA　improves　the　adaptability　and　robustness　of　the　algorithm　by　dynamically　adjusting　the　crossover　and　mutation　probability.　AGA　has　better　flexibility　and　adaptability　in　the　design　of　high-rise　residential　buildings　and　is　expected　to　provide　more　optimized　solutions　for　solving　complex　design　problems.　The　findings　of　this　study　provide　a　useful　reference　for　innovation　and　sustainable　development　in　the　field　of　high-rise　building　design　and　also　provide　practical　methods　and　tools　for　the　application　of　GA.　©　2024　Youwei　Huang　and　Xin　Zhang.,　published　by　Sciendo.