Coordinated operation of multi-boom rock jumbos in large-scale tunneling involves strong coupling between task allocation and path planning, along with multiple conflicting objectives. To address these challenges, this study proposes a multi-objective co-optimization framework based on an improved non-dominated sorting genetic -algorithm Ⅱ (NSGA-Ⅱ). The framework simultaneously minimizes total makespan, workload variance, and overall energy consumption while enforcing practical engineering constraints through a penalty-based mechanism. Considering drill-and-blast operational characteristics, the construction process is divided into three stages, namely cut holes, auxiliary holes, and perimeter holes, and the drilling sequence is formulated as a multi-objective optimization problem. Algorithm performance is enhanced through a hybrid evolutionary mechanism combining elite population initialization with adaptive genetic operators, and an embedded path optimization module generates efficient movement paths for drill booms within their assigned task subsets. The technique for order of preference by similarity to ideal solution (TOPSIS) is employed for final solution selection. Experimental results demonstrate that: (1)The proposed method produces collaborative, spatially partitioned, collision-free work plans that are practically executable. (2)Compared with the conventional greedy algorithm and the multi-objective evolutionary algorithm based on decomposition, the optimal makespan is reduced by approximately 9.27% and 14.68%, respectively, and the workload variance is reduced to 0.69, markedly improving parallel efficiency and workload balance in multi-boom operations.