A　large　number　of　cases　show　that　the　multi-objective　optimization　method　can　significantly　improve　building　performance.　The　method　for　multi-objective　building　performance　optimization　(BPO)　design　has　achieved　rapid　development　in　recent　years.　However,　the　BPO　method　still　needs　to　be　improved.　Specifically,　weak　interaction　between　the　optimization　process　and　the　decision-making　process　results　in　low　optimization　efficiency,　which　limits　the　widespread　application　of　the　optimization　method　in　early　design　stage.　In　this　paper,　a　new　interactive　BPO　mode　is　explored　to　strengthen　the　interaction　between　the　optimization　process　and　decision-making　process,　and　a　preference-based　multi-objective　BPO　method　is　proposed　to　account　for　designers＇　decision　preferences　during　the　optimization　process,　making　the　objective　more　controllable,　improving　the　optimization　efficiency　and　ensuring　the　diversity　of　solutions.　Firstly,　this　paper　illustrates　the　proposed　method　in　detail,　defines　the　concept　of　performance　preference,　expounds　the　flow　of　the　preference-based　multi-objective　optimization　algorithm,　and　proposes　three　indicators　to　evaluate　the　algorithm,　which　includes　convergence　speed,　preference　satisfaction　rate,　and　diversity　measurement.　Secondly,　through　testing　and　comparison,　it　is　found　that　the　proposed　preference-based　algorithm　has　advantages　over　the　non-preference　optimization　algorithm　(represented　by　the　NSGA-II　algorithm).　The　proposed　method　leads　to　faster　convergence　and　higher　preference　satisfaction,　so　it　is　more　suitable　for　the　BPO　process　in　the　early　design　stage.　Specially,　the　proposed　method　can　achieve　100%　preference　satisfaction　rate　with　only　2400　simulations,　while　the　non-preference　method　can　only　achieve　20%　preference　satisfaction　rate　after　5800　simulations.　In　this　paper,　a　preference-based　multi-objective　BPO　method　is　proposed　to　make　the　optimization　process　closely　interact　with　the　decision-making　process　and　make　the　design　preferences　be　accounted　during　the　BPO　process,　thereby　improving　the　optimization　efficiency.　In　addition,　this　study　first　proposes　two　indicators　to　measure　the　quality　of　optimization　results:　preference　satisfaction　rate　and　diversity　measurement.　This　study　aims　to　guide　the　development　of　BPO　methods　towards　providing　high　satisfaction　rate　and　high　quality　optimization　results.