<p>Maintaining the balance between feasibility, convergence, and diversity is a challenge in constrained multi-objective optimization problems for the operation performance of the wastewater treatment process (WWTP). This paper proposes an improved two-archive evolutionary algorithm for constrained multiobjective optimization (C-TAEA) based on Intergenerational Information Guidance, namely IIG-C-TAEA. First, the proposed algorithm proposes a mating selection mechanism to fairly screen elite solutions in three aspects: feasibility, convergence, and diversity. Then, the algorithm employs intergenerational information to guide offspring iteration by adjusting crossover and mutation probabilities and establishing neighborhoods between intergenerational populations. Finally, an archive-driven mechanism is introduced to select high-quality solutions and to establish an association between archives based on population information, enhancing the balance of diversity, convergence, and feasibility. The efficacy of the proposed algorithm is confirmed through comparative analyses conducted on benchmark problems and wastewater optimization problems, demonstrating its superiority over other constrained multi-objective optimization evolutionary algorithms in comparison.</p>