Multiple point measurements reduce the uncertainty of the spatial extrapolation of the wind climate from the measurement position to the considered turbine position and overall the AEP uncertainty. A WindScanner system consisting of two synchronized scanning lidar potentially represents a cost-effective solution for multi-point measurements especially in complex terrain. However, the system limitations (e.g., expected range) and limitations imposed by the site (e.g., orography) are detrimental to the installation of WindScanners and number and location of measurement positions. To simplify the process of finding suitable measurement positions and the associated installation locations of WindScanners we devised campaign planning workflow. The workflow consists of four phases. In the first phase, based on a preliminary wind farm layout, we generate optimize measurement positions using greedy algorithm and measurement representativeness radius. In the second phase, we generate several Geographical Information System (GIS) layers of information such as exclusion zone, line-of-sight (LOS) blockage, and lidar range maps. These GIS layers are then used in the third phase to find optimum positions of the WindScanners with respect to the measurement positions considering the WindScanner measurement uncertainty. In the fourth phase, we optimize and generate trajectory through the measurement positions by applying traveling salesman problem (TSP) on a set of measurement points. The above described workflow has been digitalize into so-called Campaign Planning Tool (CPT) currently provided as a Python library which allows users an effective way to plan measurement campaigns with WindScanners.