This　study　solves　a　new　green　high-　seas　multimodal　transportation　route　and　speed　optimization　problem　considering　emission　control　areas.　This　study　first　formulates　a　multi-objective　mixed　integer　nonlinear　programming　model　under　different　carbon　emission　policies　and　transforms　the　nonlinear　model　into　an　equivalent　mixed-integer　linear　programming　model　according　to　the　problem　characteristics.　To　effectively　solve　the　models,　an　improved　adaptive　genetic　algorithm　(IAGA)　incorporating　the　characteristics　of　the　problem　is　proposed,　in　which　a　customized　multi-layer　coding　and　decoding　mechanism　and　an　adaptive　genetic　evolution　operator　are　proposed.　Finally,　a　case　study　from　the　high-sea　multimodal　transportation　system　in　China　is　conducted　to　demonstrate　the　viability　of　the　proposed　model　and　algorithm　and　a　sensitivity　analysis　is　also　done　for　various　time　frames　and　low-sulfur　fuel　costs.　The　numerical　experimental　results　show　that:　1)　Compared　with　a　traditional　genetic　algorithm　and　the　commercial　solver　Lingo,　the　improved　adaptive　genetic　algorithm　results　in　more　satisfactory　solutions　and　reduces　total　multimodal　transportation　costs　by　5.2%　and　3.7%.　2)　Under　the　mandatory　carbon　emissions　policy,　changes　in　emission　allowances　typically　do　not　affect　the　choice　of　the　route　made　by　an　operator,　but　only　affect　whether　the　operator　conducts　transportation　activities.　Under　the　carbon　tax　policy,　the　overall　cost　of　intermodal　transportation　is　not　significantly　affected　by　the　carbon　tax　price　increase.　Under　the　carbon　trading　policy,　multimodal　transport　options　with　different　emission　allowances　may　be　consistent.　And　3)　the　shipping　cost　can　lead　to　a　direct　proportion　to　the　price　of　low-　sulfur　fuel,　and　adopting　different　ship　speeds　inside　and　outside　the　emission　control　areas　can　bring　clear　economic　advantages.　©　2023　Science　Press.　All　rights　reserved.