The　efficient　management　of　storage　has　become　a　critical　determinant　of　operational　efficiency　due　to　the　growing　and　diverse　demand　for　steel　logistics　parks.　However,　the　current　storage　methods　consistently　result　in　significant　imbalances　in　the　loading　of　steel　products　and　operating　times　across　multiple　yards.　To　address　this　challenge,　we　propose　optimizing　the　distribution　of　steel　products　based　on　demand　levels　and　retrieval　efficiency.　Firstly,　we　present　an　optimization　problem　that　employs　demand-driven　storage　principles　to　formulate　the　scheduling　of　steel　product　storage.　Then,　we　introduce　a　novel　algorithm　called　the　Adaptive　Preferred　Evolutionary　Heuristic　(APEH)　to　tackle　this　problem.　The　results　from　our　experiments　indicate　that　this　model　effectively　enhances　the　storage　structure,　and　the　proposed　algorithm　consistently　yields　optimal　solutions　within　a　reasonable　timeframe.　©　2023　IEEE.