Push and pull search (PPS) is an approach of evolutionary constrained multi-objective optimization that involves de-composing the objective space. PPS comprises two search phases: the ‘push’ and ‘pull’ phases. In the push phase, solutions evolve towards the optimal objective trade-off ignoring constraints. Subsequently, in the pull phase, the solutions evolve towards the optimal objective trade-off satisfying constraints. Depending on the characteristics of the problem, the pull phase starts with only infeasible solutions because constraints are ignored in the previous push phase. Although the feasible solutions discarded in the push phase may have worse objective function values than the infeasible ones in the population at the end of that phase, they can still contribute to the search for optimal feasible solutions in the pull phase. This paper aims to enhance the performance of constrained multi-objective optimization based on the PPS approach. We propose PPS-DM (PPS with directed mating), which archives feasible solutions obtained in the push phase and utilizes them for the search in the pull phase. To generate a new solution, the directed mating focuses on an infeasible solution, extracts feasible solutions dominated by the focused infeasible one from the archive, and applies the differential evolution operator between them. Experimental results using LIR-CMOP test problems show that the proposed PPS-DM improves the search of the pull phase and archives totally better performance than the conventional PPS, NSGA-II, NSGA-III, and MOEA/D.