The power allocation in Vanadium Redox Flow Battery (VRB) energy storage systems faces a conflict between long-term lifespan and real-time power coupling. Using a single-layer optimization method to directly address multiple objectives simultaneously may lead to conflicts among these objectives. Therefore, this paper presents a multi-objective two-layer optimization allocation strategy. Its core is hierarchical scheduling for long/short-term goals to optimize multi-attribute objectives precisely. A two-layer model comprising an initial allocation layer and an operational optimization layer is constructed to ensure the prioritization of long-term lifespan objectives based on a predefined hierarchical structure. The initial allocation layer focuses on the long-term objective of energy storage capacity lifespan, by prioritizing minimal capacity degradation. A differential evolution algorithm is then applied to perform preliminary allocation of the total power demand. The operational optimization layer aims to achieve optimal State of Charge (SOC) balance across all units and minimize power losses. An Adaptive Multi-Objective Harris Hawks Optimizer (ASAMOHHO) based on adaptive simulated annealing is established to find the Pareto optimal solution set, and ultimately determining the real-time power allocation plan for each unit. Comparative simulations with conventional methods were conducted, and the results demonstrate that the proposed strategy provides an efficient and practical solution for efficient VRB scheduling.