The application of unmanned aerial vehicles (UAVs) in imaging reconnaissance has been an important technology for improving the reconnaissance efficiency in modern warfare. This paper studies a multi-UAV task scheduling problem for joint point and area target imaging reconnaissance, wherein an area target can be collaboratively covered by a coalition consisting of multiple heterogeneous UAVs. A mixed-integer programming model is formulated to minimize total reconnaissance time and the number of failed reconnaissance tasks. The model considers multiple complex constraints including imaging sensor capabilities, task imaging quality requirements, and time windows. A knowledge-driven task planning algorithm for collaborative heterogeneous multi-UAV reconnaissance (HMUR-KTPA) is designed to solve this problem. First, the original problem is decomposed into two phases: multi-UAV task allocation and single-UAV task sequencing. In the initial phase, an optimal coalition allocation (OCA) method and a coalition-first-assigned (CFA) algorithm are developed to generate high-quality initial solutions. In the optimization phase, four domain knowledge-driven task adjustment operators and an improved variable neighborhood descent (IVND) algorithm with four problem-specific scheduling operators are designed to search for optimal task allocation and scheduling solutions. Finally, extensive experiments and comparative studies are conducted to verify the effectiveness of the proposed approach in improving task completion rates and reducing execution time. Furthermore, several sensitivity analyses identify three critical influencing factors on the HMUR-KTPA.