Wireless sensor network (WSN) is one of the most challenging study areas in the field of information technology. Among the many challenges posed by WSN, optimization in the presence of multiple conflicting objectives is one of the most major challenges. This paper discusses the previous research work in the field of MOO WSN. This research proposes a new algorithm, Enhanced NSGA-III which is a reference based with new dynamic weight-based cluster scheduling algorithm. ENSGA uses multi-parent order crossover (MPOX) to enhance fresh child to produce the best Pareto Fronts (PF). Total number of sensor nodes, network coverage and energy consumption are considered as the three conflicting objectives for the optimization study. ENSGA exhibits promising results in comparison to the NSGA-II and NSGA-III. The possible future work is also discussed in the paper.