Proper settings of key process variables are critical to the product quality control of mass-producing batch processes. The most widely used method in searching for the optimal process condition is the model-based optimization method (MBO). However, model development could be a challenging task in many cases. The accuracy of the model may deteriorate when the process conditions are changed. A systematic, model-free optimization method (MFO) for a type of batch process with a short cycle time and low operational cost is proposed to improve the efficiency of quality control. Instead of building a quality model, a direct search for the optimum process condition using experimental measurements is applied. Optimization algorithm is implemented as well to improve the search efficiency; both the gradient-based and the gradient-free optimization methods are discussed. The simultaneous perturbation stochastic approximation (SPSA) and the simplex search algorithm are incorporated in the MFO. The NEED method was applied to the quality control of injection molding process for demonstration using the part weight, part dimension, and focal length of molded products as quality measurements. A comparison with the Kriging modeling and optimization technique is also presented. The experimental results proved the effectiveness of the MFO technique.