Constrained multiobjective optimization problems often have complex feasible regions and constrained Pareto fronts. These factors bring great challenges to current constrained multiobjective optimization evolutionary algorithms (CMOEAs). To solve this problem and further balance the objective optimization and constraint satisfaction, we propose an indexesbased and partial restart-based constrained multiobjective optimization algorithm (IRCMO). In IRCMO, a two-stage (i.e., development and enhancement) and tri-population framework is designed. IRCMO adopts the aggregative indexes-based evaluation and adaptive collaborative partial restart strategy to assist the evolution of the first and second populations. The third population is obtained by directed sampling, which is mostly located at the boundary of the feasible region and enhances the exploration ability of extreme solutions. At the end of each generation, a progressive dual-archive strategy is designed to screen the solutions distributed uniformly from three populations. Experimental results demonstrate that IRCMO is superior to the other six state-of-the-art CMOEAs on several constraint benchmark suites and real-world problems.