There　is　great　potential　for　building　performance　optimization　(BPO)　in　the　early　design　stage,　but　there　is　still　a　lack　of　methods,　algorithms,　and　tools　to　support　the　BPO　process　in　this　stage.　Through　a　comprehensive　review,　this　study　identified　three　critical　issues　that　affect　the　implementation　of　the　BPO　process　in　the　early　design　stage:　model　integration,　real-time　performance　analysis,　and　interactive　optimization　design.　This　study　provides　a　systematic　solution　to　these　three　critical　issues.　In　terms　of　model　integration,　a　feature-based　and　graph　based　3D　building　space　recognition　algorithm　is　proposed　to　automatically　convert　the　computer-aided　design　(CAD　model)　into　a　computer-aided　engineering　model　(CAE　model).　In　terms　of　real-time　performance　analysis,　a　simplified　physical　method,　an　HPC-accelerated　method,　and　an　AI-based　method　are　explored,　and　a　real-time　energy　modeling　module　and　a　real-time　daylighting　analysis　module　are　developed.　In　terms　of　the　interactive　optimization　design,　a　preference-based　multi-objective　BPO　design　algorithm　that　can　consider　user　preferences　is　proposed　to　make　full　use　of　the　decision-making　ability　of　humans　and　the　computing　power　of　machines　and　significantly　improve　the　optimization　efficiency　and　result　satisfaction.　Based　on　the　systematic　solution,　a　multi-objective　BPO　design　software,　MOOSAS,　is　developed.　MOOSAS　allows　real-time　performance　feedback,　dynamic　parameter　analysis,　and　interactive　optimization,　supporting　the　BPO　process　in　the　early　design　stage.　The　innovations　of　this　study　are　as　follows:　first,　this　study　proposes　a　systematic　solution　to　the　three　critical　issues　of　the　BPO　process,　i.e.,　model　integration,　real-time　performance　analysis,　and　interactive　optimization　design;　second,　this　study　develops　a　multi-objective　BPO　design　software　(MOOSAS)　for　the　early　design　stage.