Recently, MARL models, such as the Multi-Agent Transformer (MAT), have significantly improved performance by leveraging sequential decision-making processes, yet they do not explicitly consider the strategic importance of the order in which agents make decisions. This paper proposes an Agent Order of Action Decisions-MAT (AOAD-MAT), a novel Transformer-based actor-critic model that explicitly incorporates the sequence of action decisions into the learning process. To achieve this, AOAD-MAT introduces a dual-purpose decoder that predicts an agent’s action and the next agent to act. This subtask is integrated into a Proximal Policy Optimization (PPO) based loss function to synergistically maximize the advantage of sequential decision-making. The proposed method was validated through experiments on the StarCraft Multi-Agent Challenge and Multi-Agent MuJoCo benchmarks. The experimental results show that the proposed AOAD-MAT model outperforms existing MAT and other baseline models, demonstrating the effectiveness of adjusting the AOAD order in MARL.