For an industrial zone heavily populated by petrochemical and chemical plants, the design of an effective air-quality monitoring network (AQMN) is very important for local environmental and industrial sustainability. In this paper, a general methodology with multiobjective and deterministic optimization for AQMN design and redesign is developed. Generally, a Gaussian dispersion model is employed to create spatial pollution distributions according to historical meteorological conditions and updated emission source profiles. Then, a multiobjective mixed-integer linear programming model is developed to optimally design an AQMN, which allows the relocation of existing monitoring stations and/or the addition of new monitoring stations. The optimization will maximize the detectable air-quality threshold violation frequency (AQTVF) for potential pollution events in the studied industrial zone as Well as minimize the total budget cost for the AQMN implementation with considerations of existing monitoring station relocation, new station construction, and land use with different prices at different locations. The Pareto frontier of AQTVF versus total budget cost will also be provided for final decision making. The proposed methodology can be used to design a new AQMN or to retrofit an existing AQMN to monitor regional air quality more effectively. Multiple case studies are employed to demonstrate the method's efficacy.