Integrated energy system (IES) involving coal, by-product gas, power, etc., plays a pivotal role in the steel industry, which greatly requires a reasonable utilization scheme for avoiding extensive energy consumption and intensive environmental pollution. While due to the complex coupling relationship among the multiple energy and the contradiction that existed between the optimization of global IES and local subsystem, an effective solution for this important practical problem is still highly required in the real-world application. As such, in this article, a multicriteria evaluation and cascaded optimization framework is proposed. In order to make a comprehensive evaluation, criteria, including global energy consumption and local gas emission, are, respectively, evaluated from the perspective of both the entire IES and the by-product gas subsystem. Then, a cascaded optimization model is established accordingly, where a reinforcement learning based cascaded algorithm is also designed for efficiently and effectively acquiring an optimal tradeoff as the final solution. The following experiments carried out on practical industrial data clearly demonstrated that the proposed framework gives a scientifically reasonable as well as practically available energy utilization scheme, which is beneficial for energy-saving and emission reduction in practical application.