Unmanned aerial vehicle (UAV) communication has been recognized as an appealing technology for the efficient data collection in Internet of Things (IoT) networks. Whereas, the majority of existing data collection strategies of UAV-aided IoT networks with wireless power transfer (WPT) or non-orthogonal multiple access (NOMA) technology are mostly only have a single UAV, which cannot meet the requirement of massive IoT networks. Thus, we propose to maximize the minimum data collection rate of UAVs from the IoT devices (IoTDs) in a NOMA-based dual-UAV data collection system by jointly optimizing the UAVs’ trajectories, IoTDs’ scheduling and transmit power under the UAVs’ flight speed constraint, and the IoTDs’ energy harvesting constraint. To solve this non-convex problem, we resort to the alternating optimization and successive convex approximation to convert the original problem into a convex form, and then propose a low-complexity NOMA-based dual-UAV data acquisition algorithm (DUDCA) to settle the transformed problem. Abundant simulation results are given to verify that the proposed DUDCA can effectively enhance the maximization of minimum throughput compare to the benchmark strategies with fixed trajectory or random scheduling of IoTDs.