Traditional　evolutionary　algorithms　(EAs)　often　require　a　large　number　of　function　evaluations　(FEs)　to　solve　expensive　constraint　optimization　problems　(ECOPs),　leading　to　high　computational　costs.　To　address　this　challenge,　this　paper　proposes　a　fast　generalized　surrogate−assisted　evolution　algorithm　based　on　a　multi-strategy　hybrid　sparrow　search　algorithm　(FGSAEA-MSHSSA).　The　proposed　algorithm　introduces　an　innovative　model　management　approach　that　integrates　both　global　and　local　surrogate　models.　The　proposed　algorithm　constructs　the　global　surrogate　model　using　an　ensemble　surrogate　method　and　introduces　a　variance　and　distance　(VD)　criterion　to　select　appropriate　individuals　for　exact　evaluation,　effectively　guiding　the　evolutionary　search　process.　It　also　proposes　the　concept　of　a　population　trust　region　to　build　local　surrogate　models,　enhancing　the　exploration　capability　of　FGSAEA-MSHSSA.　Additionally,　the　top　feasible　mean　rule　(TFMR)　is　incorporated　to　maintain　a　balance　between　the　feasible　and　infeasible　regions　during　the　search,　improving　the　quality　of　feasible　solutions　and　significantly　accelerating　convergence.　Experimental　results　demonstrate　that　the　proposed　algorithm　outperforms　other　advanced　algorithms　on　benchmark　functions　with　varying　characteristics　and　spring　design　problems.　It　not　only　significantly　reduces　computational　costs　but　also　greatly　enhances　optimization　performance,　with　an　average　improvement　of　approximately　48.4%　compared　to　other　algorithms.　©　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature　2025.