The　optimal　control　is　an　effective　method　to　reduce　energy　consumption　for　municipal　wastewater　treatment　process.　However,　it　is　still　a　challenge　to　improve　the　effluent　qualities　and　reduce　energy　consumption　simultaneously　for　the　municipal　wastewater　treatment　process.　To　solve　this　problem,　a　data-knowledge　driven　multiobjective　optimal　control　(DK-MOC)　method　is　proposed　in　this　paper.　First,　the　expression　relationship　among　effluent　qualities,　energy　consumption　and　system　operation　state　is　established　to　obtain　the　operational　optimal　objective　model.　Second,　a　dynamic　multiobjective　particle　swarm　optimization　algorithm,　based　on　knowledge　transfer　learning　method,　is　proposed　to　obtain　the　optimal　set-points　of　control　variables　adaptively.　Finally,　the　proposed　DK-MOC　method　is　applied　to　the　benchmark　simulation　model　No.　1　(BSM1)　of　the　municipal　wastewater　treatment　process.　The　results　demonstrate　that　this　proposed　method　can　obtain　the　optimal　set-points　of　control　variables　online,　which　not　only　improve　the　effluent　qualities,　but　also　reduce　the　operation　energy　consumption　effectively.　Copyright　©　2021　Acta　Automatica　Sinica.　All　rights　reserved.