Autonomous systems often collaborate in the face of adversaries, engaging in tasks such as safe motion planning, target protection, collision avoidance, and robotic herding. To analyze these interactions, multiplayer reach-avoid differential games (MRADG) serve as effective mathematical abstractions. This research investigates an MRADG in 3D space with $n$ pursuers and $m$ evaders. The evaders aim to reach a stationary target while avoiding capture by the pursuers. The problem is formulated within the framework of differential games.

Initially, the research addresses the case where $n=m=1$ and optimal strategies are determined in feedback form. The crucial challenge of this work lies in extending this 1v1 solution to the general case of $n\geq m$. This involves a hybrid decision problem requiring a two-layered approach. The first layer employs a linear program to determine the optimal assignment of pursuers to evaders. The second layer provides optimal strategies based on the assignment scheme. First, an analytical condition is derived to determine the winning team based on initial conditions. Additionally, within the pursuer team's winning region, optimal payoffs and strategies in state feedback form are computed for all the agents. Finally, the improved performance of the proposed assignment scheme is compared to a brute-force approach prevalent in the literature.