Abstract: To improve the optimization effect and efficiency of large-scale reefer-loads scheduling in ports, this paper proposes a hierarchical iterative scheduling architecture and the multi-agent refrigerating efficiency consensus optimization strategy for reefer clusters. The reefer power model considering the thermodynamic process is established, and the reefers are clustered according to power characteristics, which reduces the control dimensions and the information interactions. Then the pre-scheduling model for the iterative optimization of dynamic price and reefer cluster consumption is established. A leader-follower refrigerating efficiency consensus algorithm for power dynamic allocation is proposed to make each reefer actively respond to the pre-scheduling strategy according to the electricity price, temperature, and cooling limits. It realizes self-optimizing operation of massive reefers and orderly load transfer. Finally, taking Rizhao Port as an example, the proposed method can reduce the electricity cost by 12.5% and increase the computational efficiency by 4 times. The deviation of the optimization results from the global optimization is reduced to 0.5%. It realizes efficient optimization of large-scale reefer loads.