This article delved into the intelligent scheduling and path planning issues of Internet of Things (IoT) technology in logistics transportation, and proposed an IoT intelligent scheduling system based on genetic algorithm (GA). The purpose of this system was to optimize logistics routes, improve transportation efficiency, and reduce costs and the impact on the environment. Three experiments were designed to demonstrate the effectiveness of this system: efficiency comparison, cost analysis, and assessment of its impact on the environment. The experimental results showed that compared with traditional scheduling methods, genetic algorithms performed well in improving scheduling efficiency, reducing logistics costs, and reducing carbon emissions. When handling 50 logistics tasks, the average completion time was reduced by about 31%, only 62.3 min; costs were reduced by about 24%, and carbon emissions were only about 70 kg of carbon dioxide equivalent. Genetic algorithms have unique advantages in intelligent scheduling and route planning, especially when dealing with complex logistics networks and dynamically changing environmental conditions. This study not only provided an efficient intelligent scheduling solution for the logistics industry, but also opened up new ideas for the future development direction of logistics systems.