Abstract: As one of the major energy-consuming processes in steel production, sintering accounts for approximately 10% of the total energy consumption of steel production. The energy consumed in the sintering process is mainly attributed to solid fuels. Additionally, in traditional sintering, optimized ore–fuel ratio is usually determined by experience, which fails to achieve a dynamic balance between raw material type and sintering process combustion consumption. In this study, we first analyze the complex physicochemical reaction processes, such as the decomposition of crystalline water, combustion of solid fuels, and oxidation and reduction of iron oxides in the sintering process, to understand the energy flow of the sintering process. We then set empirical parameters according to an actual sintering site, and we finally establish a sintering energy–mass balance model. Subsequently, the sintering energy balance constraint is embedded on the basis of the existing constraints of chemical composition, alkalinity, raw material ratio, etc. Additionally, the cost of sintering raw material is taken as the optimization target, after which a sintering batching model based on sintering energy balance is constructed; the penalty function method is used to transform the constrained problem into an unconstrained one; finally, the actual furnace charge structure of a certain steel plant is solved by using the particle swarm algorithm (PSO) to realize completely automatic dosing of sintering iron ore, flux and fuel. The simulation results show that the optimized sintering ore allocation based on the proposed PSO algorithm-led optimal sintering ore allocation model results in a suitable fuel ratio and increased energy efficiency of the sintering process. The optimal sintering ore allocation method is a compromise of various conflicting objectives; therefore, the solved ore allocation scheme is taken as the object, and the four indicators TFe, cost, S content, and solid fuel usage are integrated; additionally, the weights of each indicator are objectively obtained by using the entropy weight method according to the dispersion degree of data and information entropy of each indicator, under the principle of considering the balance of group benefit maximization and individual regret minimization. The VIKOR (Multicriteria optimization and compromise solution) method is used for compromise ranking and preference of the scheme. The final results confirm that the proposed PSO–VIKOR sintering ore allocation optimization model achieves energy saving and emission reduction in the sintering process while considering the sintering cost and quality, which is expected to help in low-carbon green development and sustainable evolution of sintering in iron and steel enterprises and achieve the double carbon target.