A challenge of using terrestrial laser scanners on jobsites is to effectively plan the scanning locations and configure scanners' technical parameters according to the domain requirements and site conditions. With time limits in the field, it is challenging to manually design and analyze scanning plans considering technical capabilities of scanners and environmental conditions (e.g., occlusions) for ensuring that all needed spatial information is captured with the required levels of accuracy and detail. In addition, the information requirements of construction tasks may vary spatially and temporally, further complicating the scan planning. These facts can lead to longer data collection time, unexpected interferences on jobsites, and missing data. This paper proposes an approach of automated laser scan planning based on sensor models of terrestrial laser scanners. We developed a sensor model capturing the relationships between various data collection parameters (e.g., data collection distances) and data quality metrics (e.g., accuracies and densities of 3D point clouds). Given the user-specified data quality requirements, an optimization algorithm can use this sensor model to identify scanning plans that minimize the data collection time while optimizing the data qualities and spatial information coverage.