Abstract: In the context of networked radar countermeasure scenarios, this paper studies the joint optimization of beam pointing and power allocation for multiple jammers against multiple radars under limited jamming resources, aiming to maximize the suppression of the enemy's networked radar detection capability. To this end, a mixed-integer nonlinear programming model is constructed, with the main objective of minimizing the joint detection probability of the radar network on the target, while satisfying physical constraints such as total jamming energy, beam pointing feasible region, and jamming pattern compatibility. Here, the suppression effect is characterized by the detection probability determined by the signal-to-interference-plus-noise ratio (SINR) received by each radar, and the jamming resource scheduling requires the coordinated optimization between discrete jamming patterns and continuous power variables. To solve this high-dimensional, non-convex, and complex optimization problem with coupled mixed variables, a chaotic-opposition-based elite-guided whale optimization algorithm (COL-WOA) is proposed. This method integrates a hybrid chaotic mapping to enhance population diversity, mirror opposition learning to improve the quality of initial solutions, and introduces an elite individual guidance mechanism to accelerate convergence, effectively overcoming the shortcomings of the standard whale optimization algorithm (WOA), such as being prone to local optima and slow convergence. Simulation experiments under different jamming energy budgets, spatial deployment configurations, and population sizes show that the proposed COL-WOA significantly outperforms the standard WOA and other typical meta-heuristic algorithms in reducing radar detection probability, improving jamming energy efficiency, and achieving stable convergence. Moreover, it can achieve a Pareto trade-off between suppression effectiveness and resource consumption by adjusting the jamming power allocation.