Product Manufacturing scheduling or Batch Sequencing (BS) in real-world pharmaceutical manufacturing poses a complex multiobjective optimization challenge, intensified by constraints and uncertain demands. These problems are typically non-linear, may leading to low convergence of feasible solutions, and the computationally intensive evaluation functions may hinder the adoption of certain techniques. Multiobjective Evolutionary Algorithms (MOEAs) have been explored due to their flexible and efficient operators that can be applied to various non-linear multiobjective problems. This study proposes enhancements to MOEA operators, specifically a selection strategy with constraint-based partition for reinsertion to improve diversity and exploration, and a greedy local search strategy incorporated into the mutation operator to enhance the exploitation of the most promising regions. These enhancements were integrated into two models, ps-re and bl-bat, both of which demonstrated significant improvements in the performance metrics compared to a reference model.