The framework of Multi-Objective Pareto collaborative optimization (MOPCO) methods is proposed and then was applied to RLV reentry Trajectory Design. One of focus idea is to use NSGA-II algorithm to search the Pareto Front. In order to avoid the huge computation cost of the NSGA-II in CO, modify the conventional sequential CO to concurrent mode by Datacenter based on neural network response surface. The mathematic test problems and RLV reentry trajectory optimization validate MOPCO’ feasibility. We extend the conventional CO to solve Pareto Front of multi-objective multidisciplinary optimization successfully.