Using　manufactured　sand　(M-sand)　as　a　replacement　of　natural　sand　is　beneficial　both　environmentally　and　economically.　However,　apart　from　basic　requirements　like　that　for　concrete　using　natural　sand,　mix　design　of　manufactured　sand　concrete　(MSC)　needs　to　take　more　factors　into　consideration　and　requires　more　efficient　optimization　due　to　relative　deficiency　in　testing　data.　This　paper　uses　86　instances　of　MSC　with　6　features　to　predict　compressive　strength　and　chloride　permeability　coefficient　(CPC)　of　MSC　by　employing　four　machine　learning　(ML)　models　(Back　propagation　(BP)　neural　network,　random　forest　(RF),　support　vector　regression　(SVR)　and　eXtreme　Gradient　Boosting　(XGBoost)).　All　four　models　have　predicted　the　compressive　strength　and　CPC　of　MSC　accurately,　with　R2　of　test　set　ranging　from　0.85　to　0.93　after　hyperparameter　optimization,　with　XGBoost　models　achieving　the　highest　R2　of　0.93　for　both.　Also,　SHapley　Additive　exPlanations　(SHAP)　analysis　indicates　that　cement　content　is　the　most　predominant　factor　to　affect　compressive　strength　and　CPC,　followed　by　M-sand　content　and　water/binder　ratio　(W/B　ratio).　Finally,　CPC,　compressive　strength　and　unit　cost　are　combined　to　construct　a　three-way　fitness　function　and　multi-objective　optimization　is　performed　using　NonDominated　Sorting　Genetic　Algorithm　2　(NSGA-II).　Based　on　multi-algorithm　comparison　and　cost-aware　multi-objective　XGBoost-NSGA-II　optimization,　the　mix　design　method　proposed　is　advantageous　in　terms　of　accuracy,　reliability　and　production　cost　compared　with　ML　models　that　employ　a　single　model,　predict　a　single　property,　and　do　not　take　in　cost　as　a　factor　for　mix　design.