To address the computational challenge posed by the “curse of dimensionality” inherent in traditional branch and bound algorithms for large-scale power grid unit commitment problems, an optimization method for iteration path search of unit commitment state is proposed. To prevent the loss of the optimal solution due to the simplification of the problem and the reduction of the feasible region, the determination of the unit state scheme is divided into a two-stage process of depth traverse and breadth iteration. Based on an initial solution, the unit dynamic priority list is used as the search direction for the unit state iteration path. In deep traverse stage, the optimal shutdown redundant units and their corresponding shutdown time are determined. Breadth iteration is then used to expand the feasible region of the problem to improve the optimality of the solution. The results of a comparative case study conducted on the IEEE 118 system and ACTIVSg10k system indicate that the proposed method effectively reduces the scale of the problem, minimizes the number of unit state attempts, and achieves efficient search and iteration of unit states, exhibiting fast computational speed, high efficiency, which has practical applicability for solving problems of large-scale unit commitment.