Optimizing　early-stage　building　design　significantly　impacts　energy　efficiency,　carbon　reduction,　and　indoor　air　quality.　While　parametric　design　coupled　with　Multi-Objective　Optimization　is　a　prevalent　approach　in　energy-focused　preliminary　design,　it＇s　important　to　note　that　the　emphasis　has　been　on　parameter　adjustments　rather　than　algorithm　modifications.　However,　altering　the　design　algorithm　can　have　a　more　fundamental　influence　on　early-stage　design,　yet　this　area　remains　relatively　under-researched.　To　aid　designers　in　producing　better　performance-oriented　designs　at　an　early　stage,　this　study　proposes　a　set　of　quantitative　index　and　methods　to　swiftly　evaluate　and　compare　the　performance　potential　of　design　algorithms.　Office　passive　design　case　studies　are　utilized　to　assess　the　performance　of　two　common　divergent　design　algorithms.　Through　solution　space　sampling,　we　analyze　their　respective　performance　boundaries　and　distributions　by　conducting　sampling　analysis　and　performance　optimization　for　each　algorithm.　Notably,　energy　consumption　differences　can　reach　up　to　5.07　kWh/(m2·a),　while　sUDI　variations　may　amount　to　25.2　%,　and　these　variances　are　contingent　upon　climate　zones.　Furthermore,　the　efficiency　and　convergence　speed　of　these　design　algorithms　during　optimization　processes　were　quantitatively　compared.　Results　indicate　that　optimal　energy　consumption　can　vary　by　up　to　2.13　kWh/(m2·a),　with　a　time　cost　ranging　from　three　to　four　times　for　approximating　an　optimal　solution.　The　key　contribution　lies　in　providing　designers　with　a　rapid　evaluation　methods　for　performance-oriented　comparison　for　different　early　design　algorithms.　©　2024