The　multiobjective　optimal　control　method　optimizes　the　performance　indexes　of　nonlinear　systems　to　obtain　setpoints,　and　designs　a　controller　to　track　the　setpoints.　However,　the　stepwise　optimal　control　method　that　independently　analyzes　the　optimization　process　may　obtain　unfeasible　and　difficult　to　track　setpoints,　which　will　reduce　the　operation　and　control　performance　of　the　systems.　To　solve　this　problem,　a　multiobjective　integrated　optimal　control　(MIOC)　strategy　is　proposed　for　nonlinear　systems　in　this　article.　The　main　contributions　of　MIOC　are　threefold.　First,　in　the　framework　of　multiobjective　model　predictive　control,　an　integrated　control　structure　with　a　comprehensive　cost　function　and　a　collaborative　optimization　algorithm　is　designed　to　achieve　the　coordinate　optimal　control.　Second,　for　the　time　inconformity　of　setpoints　and　control　laws　caused　by　the　characteristic　of　tracking　control,　the　different　prediction　horizons　are　designed　for　the　comprehensive　cost　function.　Then,　the　collaborative　optimization　algorithm　is　proposed　for　the　comprehensive　cost　function　to　achieve　the　integrated　solution　of　setpoints　and　control　laws　to　enhance　the　operation　and　control　performance　of　nonlinear　systems.　Third,　the　stability　and　control　performance　analysis　of　MIOC　is　provided.　Finally,　the　proposed　MIOC　method　is　applied　for　a　nonlinear　system　to　demonstrate　its　effectiveness.