To address the lack of research on condition-based maintenance optimization and incentive mechanisms within the performance warranty framework, this study focuses on a repairable system governed by an Inverse Gaussian degradation process. By integrating preventive inspection measures and an availability-centered reward-penalty mechanism, a novel performance warranty decision model is constructed. The model takes the initial inspection interval, subsequent inspection intervals, and preventive maintenance threshold as decision variables, with the objective of maximizing the manufacturer's warranty profit. A particle swarm optimization algorithm is employed to solve the model, followed by a case study. Experimental results demonstrate that compared to cost-minimization warranty strategy and non-differentiated periodic inspection strategy, the proposed performance warranty strategy achieves 7.9% and 21.5% improvements in manufacturer warranty profits, respectively, validating its effectiveness. Further sensitivity analysis increases the applicability of performance warranty strategy.