Catalytic　distillation　was　used　to　promote　target　reaction　of　oil　alkylation　desulfurization,　and　the　process　was　modeled　and　analyzed　with　a　multi-objectives　optimization　method.　An　equilibrium　stage　model　for　catalytic　distillation　of　alkylation　desulfurization　was　first　established　and　solved　using　Aspen　Plus.　A　multi-objective　optimization　model　for　increasing　the　alkylation　reaction　selectivity,　decreasing　the　total　stages　and　operation　cost　was　then　established,　which　was　solved　using　non-dominated　sorting　genetic　algorithm　II　(NSGA-II).　Finally,　the　multi-objective　optimized　Pareto　front　and　Pareto　solution　set　of　the　alkylation　catalytic　distillation　desulfurization　process　were　obtained.　The　results　show　that　there　is　a　linear　relationship　between　feed　location　and　the　number　of　rectifying　stages　by　analyzing　the　results　in　the　Pareto　set.　The　decrease　of　reaction　stages　and　the　increase　in　selectivity　come　at　the　cost　of　increased　operating　costs,　and　the　number　of　stripping　stages　shows　little　effect　on　reaction　selectivity　and　operation　cost.　Comparing　with　the　optimized　result　of　sensitivity　analysis,　the　conversion　and　selectivity　of　the　alkylation　reaction　are　improved,　and　sulfur　contents　in　distillate　and　operation　cost　are　reduced　by　the　multi-objective　optimization　method.　These　results　prove　the　effectiveness　of　using　multi-objective　optimization　in　catalytic　distillation　alkylation　desulfurization　processes.　©　2020,　Editorial　Board　of　＇Journal　of　Chemical　Engineering　of　Chinese　Universities＇.　All　right　reserved.