Scheduling plays the most important role in manufacturing industries, especially in minimizing operation costs. The most critical issue of a single-line production environment is determining the schedule of multiple products in order to cope with conflicting objectives, which has been subject to recent research studies. Therefore, a new hybrid method, the integration of Multi-objective Particle Swarm Optimization (MOPSO) Algorithm and Non-sorted Genetic Algorithm-II (NSGA-II), is developed for solving single-line production environments to cope with conflicting objectives between minimizing total makespan, tardiness and idle time in this study. Additionally, uncertainty in machine breakdown time and machine repair time are incorporated in this study in order to ensure the sustainability of the operation. The proposed method was tested and validated with 40 benchmark problems after they had been modified. The success of the proposed model relied on the minimum value of total makespan (Cmax), tardiness (T) and idle time (I). The proposed integrated model has demonstrated a good ability to schedule the production and maintenance process in order to achieve a trade-off between scheduling production and solving multi-objective models for single-line production scheduling problems. It has also provided a beneficial guide to the company in determining the product schedule while it improves the state-of-the-art in terms of required computational effort and quality of solutions.