In the present study, a scalable multi-objective optimization strategy of industrial purified terephthalic acid (PTA) oxidation process is proposed to improve the industrial operation efficiency. The model is based on the fundamental oxidation mechanism and historical industrial data, which is structured into two series ideal continuously stirred tank reactor (CSTR) models. Generally, the optimal objective of industrial operation is to produce more and polish the quality of the production with minimizing the consumption of both energy and materials. In the PTA oxidation process, the most important undesired intermediate product generated and accompanied with the product is 4-carboxy-benzaldehyde (4-CBA), and its content is usually regarded as a criterion to judge the quality of the product. The yield of PTA oxidation process is represented by the inlet rate of the reactor, which is also one of the decision variables in the model. In order to be better applied in different industrial operation cases, a four level scalable operation strategy is proposed in the steady multi-objective optimization problem. In each operation cases, different combinations of decision variables are presented according to the industrial practice. The steady multi-objective optimization algorithm applied in this study is based on evolutionary algorithm for its natural characteristics, which is proposed by the authors and named as neighborhood and archived genetic algorithm (NAGA).