This　research　proposed　an　integrated　strategy　for　building　performance　optimization　from　the　whole　life　cycle　perspective　to　explore　the　optimal　building　scheme.　After　the　feature　elimination,　the　ensemble　learning　model　(ELM)　was　trained　to　obtain　a　high-precision　model　for　predicting　life　cycle　carbon　emissions　(LCCE),　life　cycle　costs　(LCC),　and　indoor　discomfort　hours　(IDH).　Then,　the　optimal　optimization　algorithm　was　selected　among　three　different　optimization　algorithms.　Finally,　the　best　building　scheme　was　chosen　according　to　the　newly　proposed　solution.　The　results　showed　that　the　ELM　could　achieve　high　prediction　efficiency　by　combining　input　feature　evaluation　and　screening,　multi-sampling　methods,　and　hyperparameter　optimization.　The　R2　value　of　ELM　can　reach　0.980,　while　the　Two-Archive　Evolutionary　Algorithm　for　Constrained　multi-objective　optimization　(C-TAEA)　was　the　optimal　optimization　algorithm.　The　best　equilibrium　solution　proposed　in　this　study　solved　the　problem　of　different　optimization　ranges　of　different　objectives　and　maximized　the　optimization　value.　Finally,　the　best　equilibrium　scheme　reduced　the　LCCE　by　34.7%,　the　LCC　by　13.9%,　and　the　IDH　by　26.6%.　Therefore,　this　strategy　can　efficiently　optimize　building　objectives　and　produce　a　more　balanced　and　optimal　building　scheme,　thus　making　it　widely　applicable　in　building　performance　optimization.　©　2022　Elsevier　Ltd