With the significant improvement in the task performance ability of Large Language Models (LLMs) through natural language processing, the possibility that computers can replace tasks previously thought to be performable only by humans is becoming increasingly realistic. This study seeks to evaluate the effectiveness of LLMs in solving transportation mission planning optimization problems, focusing on the Traveling Salesman Problem (TSP) as a typical example. The primary research question of this study is: “Can LLMs provide a feasible solution to the transport mission planning optimization problem?” To address this, we employ a controlled experiment as the main methodology using the Optimization by PROmpting (OPRO) (Yang et al., 2023) approach to tackle the Traveling Salesman Problem (TSP). We also explore the potential capabilities of large language models (LLMs) and investigate performance improvements by applying Chain-of-Thought (CoT) (Wei et al., 2022) prompting techniques. The CoT technique enables LLMs to systematically execute a series of logical steps in the problem-solving process, leading to accurate and efficient solutions for more complex optimization problems. As a result of the experiment, it is confirmed that performance is significantly improved through the CoT technique (Wei et al., 2022) as the situation becomes more complicated, with the optimality gap decreasing by 20 percentage points at 15 nodes. This highlights the contribution of the quality of task descriptions provided to LLMs. In conclusion, this study evaluated the optimization problem-solving ability of LLMs for the TSP based on the OPRO (Yang et al., 2023) and the CoT technique (Wei et al., 2022). This research presents a new approach for transport mission planning optimization, indicating the need for deeper analysis in follow-up studies to assess the scope of application and performance of LLMs. Follow-up research will further enhance LLMs’ optimization problemsolving ability and usability in actual transport mission planning.