Interference resource optimization is a prerequisite for air defense suppression mission planning, and the degree of optimization of interference resources directly determines the quality of the interference results. In this problem, this paper establishes an interference resource optimization model with radar detection probability, interference effectiveness, and interference bandwidth utilization as the objective functions. Then, in the solution process, to address the issues of the Whale Optimization Algorithm (WOA) easily falling into local optima and low convergence accuracy, the BIO-WOA (Bernoulli Chaotic mapping In-nonlinear Factors and Opposition-based Learning Improved Whale Optimization Algorithm, BIO-WOA) is proposed. First, the population initialization is completed using Bernoulli chaotic mapping based on the whale optimization algorithm, increasing the diversity and uniformity of solutions and enhancing the algorithm’s global search capability. Then, a nonlinear convergence factor is proposed to balance the local and global search capabilities of the algorithm. Subsequently, the centroid opposition-based learning is used to generate mutated whales, improving the algorithm’s ability to escape local optima. Finally, the effectiveness of the algorithm is verified through test functions and simulation experiments.