To　comply　with　the　effluent　standards　and　growing　demands　for　safety　and　reliability,　the　operation　of　wastewater　treatment　processes　(WWTPs)　has　been　considered　as　a　multiobjective　control　problem.　In　this　article,　a　data-driven　multiobjective　predictive　control　(MOPC)　method　is　developed　to　deal　with　the　conflicting　control　objectives　to　improve　the　operation　performance　of　WWTPs.　The　main　contributions　of　MOPC　are　three　folds:　first,　a　multiobjective　control　strategy　is　developed　in　the　design　of　MOPC.　And　an　adaptive　fuzzy　neural　network　identifier,　using　the　relevant　process　data,　is　designed　to　catch　the　nonlinear　behaviors　of　WWTPs.　Second,　a　transfer　multiobjective　optimization　algorithm　(TMOOA)　is　developed　to　obtain　the　optimal　solutions　of　the　conflicting　control　objectives.　The　major　advantage　of　TMOOA　is　its　low　computational　cost,　which　is　realized　by　avoiding　the　computation　of　Pareto　fronts.　Third,　the　stability　of　MOPC　has　been　given　in　detail.　Meanwhile,　the　benefits　and　feasibility　of　MOPC　are　confirmed　on　the　benchmark　simulation　model　no.&　x00A0;2.　The　results　further　demonstrate　the　effectiveness　of　the　proposed　control　method.