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Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. The optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this article models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee-based active learning (CAL) assisted optimal computing budget allocation-based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., the tracking model, tracking method, and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.