Due　to　its　demonstrated　efficacy,　optimal　control　has　extensive　application　in　nonlinear　systems.　However,　in　the　optimal　control　process,　the　time　delays　in　the　optimization　objectives　makes　the　optimization　problem　difficult　to　solve.　To　improve　the　optimal　control　performance,　a　coevolution-based　robust　optimal　control　(C-TDROC)　method　is　designed.　First,　a　data-driven　estimation　strategy　is　proposed　to　approximate　the　optimal　objectives　of　nonlinear　systems.　Then,　the　approximation　errors　caused　by　time　delays　are　described　as　uncertain　representations　of　system　states.　Second,　a　coevolution-based　robust　optimization　(CRO)　algorithm　is　developed　to　solve　the　optimal　set　points　of　system　states.　This　algorithm　generates　two　coevolutionary　particle　swarms　in　robust　time　delay　intervals　to　improve　the　robustness　of　optimal　set　points.　Third,　an　adaptive　time　delay　controller　is　proposed　for　tracking　the　optimal　set　points.　Then,　the　Lyapunov-Krasovskii　functionals　are　employed　to　ensure　the　stability　of　C-TDROC.　The　experiments　on　a　time-delay　nonlinear　system　and　a　time-delay　biochemical　reaction　process　are　carried　out　to　prove　the　availability　of　C-TDROC.