With the continuous development and technological advances in the modern industrial field, large component measurement techniques are becoming increasingly important in various fields. Particularly in areas such as large machinery and equipment, aerospace, etc., accurately measuring and evaluating the dimensions and shapes of parts, components, and systems is critical to ensuring product quality, meeting design requirements, and ensuring safety. Among them, station planning plays a key role in large component measurement tasks, and it directly affects the overall accuracy and efficiency of the entire measurement task. Currently, the station planning of large component measurement often relies on experienced surveyors, which leads to an increase in the time and labor cost of the measurement and the instability of the measurement results. Secondly, the traditional method of station planning for large component measurement is often time-consuming and inefficient, lacks theoretical basis and evaluation methods, and is prone to problems such as large number of stations, high number of station transfers and low measurement efficiency, which can not meet the needs of modern manufacturing industry for fast and efficient measurement. In view of the above-mentioned large-scale component multi-sensing system station planning, due to the diversification of system measurement accessibility models and the imbalance of multi-system measurement accuracy, the combined measurement station setting relies heavily on the experience of surveyors and continuous attempts to obtain suitable stations. To solve the problem, this paper proposes a combined measurement station planning method for multi-sensor systems. Firstly, considering the tooling occlusion issue, based on the combined measurement accessibility model in the collaborative measurement field, we establish an initial value solving model for tooling-affected station positions using the Remora optimization algorithm. This model calculates the initial values of measurement stations in the combined measurement system; secondly, addressing the precision constraint issue, we establish a collaborative measurement accuracy model. We formulate an optimization objective function that minimizes the weighted residual values of the observation data and the vector angular measurement errors. We optimize the scaling factor to achieve the best accuracy in station coordinates; finally, a certain target simulator satisfies the position, posture initial assembly and adjustment accuracy requirements are taken as an example. A combined measurement station planning experiment was conducted. The root mean square error of the measurement data after optimization is 0.032 mm. Compared with the measurement planning before optimization, the position measurement accuracy increased by 34%, and the angle measurement accuracy increased by 9.5 %. This method provides improvements in methods for the rapid and precise detection as well as station planning efficiency of components, parts, and systems in large-scale structures. It offers valuable references for further research and applications in the field of measurement.