Aiming　at　the　problem　that　the　traditional　open　channel　water　level　predictive　control　model　cannot　consider　the　limitation　of　the　number　of　gate　adjustments,　this　paper　adds　the　flow　adjustment　penalty　to　the　previous　predictive　control　targets　and　constructs　a　multi-target　open　channel　water　level　predictive　control　model,　then　a　genetic　algorithm　with　elite　sorting　strategy　is　used　to　solve　the　complex　optimization　problem.　The　model　is　tested　on　a　simulation　model　of　the　last　six　pools　of　the　South-to-North　Water　Diversion　Middle　Route　Project　to　analyze　the　model　reliability.　The　results　show　that　in　the　two　test　conditions,　compared　with　the　traditional　predictive　control　model,　the　multi-objective　predictive　control　model　in　this　paper　can　maintain　the　similar　water　level　control　effect　while　reducing　the　gate　control　times　by　43%　and　52%;　moreover,　taking　the　genetic　algorithm　as　the　solution　method　can　consider　the　minimum　flow　variation　constraint　caused　by　the　dead　zone　of　the　gate　and　generate　a　more　reasonable　control　plan　when　the　flow　needs　to　be　fine-tuned　in　advance.　This　paper　also　shows　the　feasibility　of　constructing　a　multi-objective　predictive　control　model　based　on　a　state　prediction　model　and　using　heuristic　algorithms　to　solve　the　optimization　problem.　©　2022,　China　Water　Power　Press.　All　right　reserved.