Submodular　maximization　has　been　a　central　topic　in　theoretical　computer　science　and　combinatorial　optimization　over　the　last　decades.　Plenty　of　well-performed　approximation　algorithms　have　been　designed　for　the　problem　over　a　variety　of　constraints.　In　this　paper,　we　consider　the　submodular　multiple　knapsack　problem　(SMKP).　In　SMKP,　the　profits　of　each　subset　of　elements　are　specified　by　a　monotone　submodular　function.　The　goal　is　to　find　a　feasible　packing　of　elements　over　multiple　bins　(knapsacks)　to　maximize　the　profit.　Recently,　Fairstein　et　al.　[ESA20]　proposed　a　nearly　optimal　(1　−　e−1　−　)-approximation　algorithm　for　SMKP.　Their　algorithm　is　obtained　by　combining　configuration　LP,　a　grouping　technique　for　bin　packing,　and　the　continuous　greedy　algorithm　for　submodular　maximization.　As　a　result,　the　algorithm　is　somewhat　sophisticated　and　inherently　randomized.　In　this　paper,　we　present　an　arguably　simple　deterministic　combinatorial　algorithm　for　SMKP,　which　achieves　a　(1　−　e−1　−　)-approximation　ratio.　Our　algorithm　is　based　on　very　different　ideas　compared　with　Fairstein　et　al.　[ESA20].　©　Xiaoming　Sun,　Jialin　Zhang,　and　Zhijie　Zhang;