The　optimization　control　of　nonlinear　systems　is　crucial　to　improve　system　performance.　However,　uncertain　disturbances　of　cost　functions　and　system　states　make　it　difficult　to　solve　optimal　set　points　and　realize　stability　control.　For　this　problem,　a　disturbance-estimation-based　robust　optimization　control　(DERO-C)　scheme　is　designed.　First,　a　data-driven　disturbance　distribution　evaluation　strategy　is　proposed　to　detect　the　deviations　in　cost　functions　caused　by　disturbances.　Then,　the　deviations　are　employed　to　approximate　the　distribution　of　uncertain　disturbances.　Second,　an　adaptive　robust　evolutionary　algorithm　is　designed　to　solve　the　optimization　problem.　Then,　the　adaptive　robustness　indexes,　derived　from　the　disturbance　distribution,　are　designed　to　guide　the　evolutionary　process　to　improve　the　robustness　of　optimal　set　points.　Third,　a　sliding　mode　controller　with　an　adaptive　reaching　law　is　employed　to　track　the　optimal　set　points　Finally,　the　optimal　control　performances　of　DERO-C　are　tested　in　the　wastewater　treatment　process.