The balance of convergence, diversity and feasibility is a difficulty for the constrained multi-objective evolutionary algorithms. Thus, a collaborative constrained multi-objective algorithm (ConMOEA) is proposed, which integrates the advantages of the adaptive geometry estimation based MOEA (AGE-MOEA) and the non-dominated sorting genetic algorithm(NSGA II). Firstly, the Deb constraint dominance is applied to sort the combined population. Then the individuals in critical layer are selected according to the maximum crowding distance or individual survival score. Finally, a new population is formed that can fast approach the Pareto front and has good distribution. The effectiveness of the proposed algorithm is validated by comparing with NSGA-II-CDP, C-TAEA, PPS, ToP, A-NSGA-III, AGE-MOEA on the DOC test suit. And the performance of algorithms is evaluated by the inverted generational distance (IGD) and hypervolume (HV). The simulation results show that the ConMOEA has better convergence and diversity.