This　study　utilizes　the　genetic　algorithm　(GA)　and　Levenberg-Marquardt　(L-M)　algorithm　to　optimize　the　parameter　acquisition　process　for　two　commonly　used　viscoelastic　models:　2S2P1D　and　Havriliak-Negami　(H-N).　The　effects　of　the　various　combinations　of　the　optimization　algorithms　on　the　accuracy　of　the　parameter　acquisition　in　these　two　constitutive　equations　are　investigated.　Furthermore,　the　applicability　of　the　GA　among　different　viscoelastic　constitutive　models　is　analyzed　and　summarized.　The　results　indicate　that　the　GA　can　ensure　a　correlation　coefficient　of　0.99　between　the　fitting　result　and　the　experimental　data　of　the　2S2P1D　model　parameters,　and　it　is　further　proved　that　the　fitting　accuracy　can　be　achieved　through　the　secondary　optimization　via　the　L-M　algorithm.　Since　the　H-N　model　involves　fractional　power　functions,　high-precision　fitting　by　directly　fitting　the　parameters　to　experimental　data　is　challenging.　This　study　proposes　an　improved　semi-analytical　method　that　first　fits　the　Cole-Cole　curve　of　the　H-N　model,　followed　by　optimizing　the　parameters　of　the　H-N　model　using　the　GA.　The　correlation　coefficient　of　the　fitting　result　can　be　improved　to　over　0.98.　This　study　also　reveals　a　close　relationship　between　the　optimization　of　the　H-N　model　and　the　discreteness　and　overlap　of　experimental　data,　which　may　be　attributed　to　the　inclusion　of　fractional　power　functions　in　the　H-N　model.