Optimal　controls　receive　much　attention　owing　to　their　remarkable　performance　for　nonlinear　systems.　However,　unknown　time-delay　disturbances　in　the　optimal　control　process　make　it　difficult　to　find　high-quality　optimal　set　points.　To　address　this　problem,　a　hybrid　evolutionary　robust　optimization-based　optimal　control　(HERO-OC)　method　is　proposed　to　reduce　the　negative　effect　of　time-delay　disturbances　and　enhance　the　performance　of　optimal　set　points.　First,　a　datadriven　time-delay　disturbance　observer　(DTDO),　based　on　fuzzy　neural　networks,　is　designed　to　describe　the　time-delay　disturbances　of　optimal　objectives.　Then,　the　expressions　and　intervals　of　time-delay　disturbances　can　be　estimated　to　improve　the　accuracy　of　optimal　objectives.　Second,　a　hybrid　evolutionary　robust　optimization　(HERO)　algorithm,　which　combines　an　expectation　robust　optimization　strategy　with　a　min-max　evolutionary　robust　optimization　strategy,　is　proposed　to　solve　optimal　set　points.　Then,　the　average　robustness　and　conservative　of　robustness　optimal　set　points　can　be　improved.　Third,　an　adaptive　time-delay　controller　using　Lyapunov-Krasovskii　functionals　is　proposed　to　track　optimal　set　points.　Then,　control　accuracy　is　improved　while　ensuring　control　stability.　Finally,　the　superior　performance　of　HERO-OC　is　compared　with　some　novel　optimal　control　methods　in　a　second-order　nonlinear　system　and　a　wastewater　treatment　process.