Existing regulation methods for industrial parks seldom balance the multi-objective synergies among carbon emissions, economic benefits, and power grid balance. Therefore, an optimized scheduling method for industrial parks that considers carbon trading and demand response is proposed. Firstly, an improved stepped carbon trading mechanism is constructed based on the carbon trading price mechanism in the actual electricity market to incentivize low-carbon operation of resources. Secondly, in response to the massive and heterogeneous nature of flexible resources within the park, a mathematical model of adjustable resource response is established to depict resource response behavior and cost characteristics. Furthermore, an optimized operation model for industrial parks is constructed, with economic benefits and carbon emissions as the optimization objectives, to perform multi-resource collaborative scheduling and fully leverage resource complementarity. Finally, a multi-objective particle swarm optimization (MOPSO) algorithm and a comprehensive membership degree method are adopted to seek multi-objective solutions. The case study results demonstrate the significant value of the proposed method in industrial parks.