Multiobjective　optimal　control　(MOC)　optimize　multiple　performance　indices　of　nonlinear　systems　to　obtain　setpoints,　and　design　the　controller　to　track　the　setpoints.　However,　if　the　feasibility　of　the　controller　is　not　considered,　untraceable　setpoints　may　be　obtained.　Furthermore,　the　performance　of　data-driven　MOC　may　be　degraded　due　to　insufficient　data.　To　address　this　problem,　a　data-knowledge-driven　multiobjective　integrated　optimal　control　(DK-MIOC)　method　is　proposed　in　this　article.　First,　an　integrated　optimal　control　(IOC)　framework　is　designed　that　integrates　a　cost　function　for　both　system　performance　and　tracking　error.　Then,　the　feasibility　of　the　controller　can　be　considered　simultaneously　while　solving　for　the　optimal　setpoints.　Second,　a　data-knowledge-driven　model　is　incorporated　into　this　framework　to　predict　future　dynamics.　Then,　the　performance　indices　can　be　accurately　predicted　even　with　insufficient　data.　Third,　a　collaborative　optimization　algorithm　is　implemented　to　determine　setpoints　and　control　laws.　Consequently,　the　operational　control　performance　of　the　nonlinear　system　is　enhanced.　Furthermore,　the　stability　of　the　DK-MIOC　strategy　is　also　analyzed.　Finally,　DK-MIOC　is　tested　on　a　conventional　nonlinear　system　and　a　wastewater　treatment　process　(WWTP)　to　validate　its　effectiveness.