With　the　escalating　severity　of　environmental　pollution　caused　by　effluent,　the　wastewater　treatment　process　(WWTP)　has　gained　significant　attention.　The　wastewater　treatment　efficiency　and　effluent　quality　are　significantly　impacted　by　effluent　scheduling　that　adjusts　the　hydraulic　retention　time.　However,　the　sequential　batch　and　continuous　nature　of　the　effluent　pose　challenges,　resulting　in　complex　scheduling　models　with　strong　constraints　that　are　difficult　to　tackle　using　existing　scheduling　methods.　To　optimize　maximum　completion　time　and　effluent　quality　simultaneously,　this　article　proposes　a　restructured　set-based　discrete　particle　swarm　optimization　(RS-DPSO)　algorithm　to　address　the　WWTP　effluent　scheduling　problem　(WWTP-ESP).　First,　an　effective　encoding　and　decoding　method　is　designed　to　effectively　map　solutions　to　feasible　schedules　using　temporal　and　spatial　information.　Second,　a　restructured　set-based　discrete　particle　swarm　algorithm　is　introduced　to　enhance　the　searching　ability　in　discrete　solution　space　via　restructuring　the　solution　set.　Third,　a　constraint　handling　strategy　based　on　violation　degree　ranking　is　designed　to　reduce　the　waste　of　computational　resources.　Fourth,　a　Sobel　filter　based　local　search　is　proposed　to　guide　particle　search　direction　to　enhance　search　efficiency　ability.　The　RS-DPSO　provides　a　novel　method　for　solving　WWTP-ESP　problems　with　complex　discrete　solution　space.　The　comparative　experiments　indicate　that　the　novel　designs　are　effective　and　the　proposed　algorithm　has　superior　performance　over　existing　algorithms　in　solving　the　WWTP-ESP.　IEEE