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Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated. Full article