The Internet of Things connects physical objects through sensor devices with multiple functionalities. At the planning stage of deploying an IoT system, we are concerned about sensor selection in the IoT system, which allocates predefined IoT services to multiple sensor devices so as to optimize one or more objectives associated with these allocations, under energy and distance constraints. The sensor selection problem that optimizes a utility function in other applications has been shown to be NP-hard, and the number of IoT services concerned is enormous in practice. Hence, it is suitable to apply evolutionary algorithms (EAs) for solving the large-scale problem with multiple objectives. Recently, the paradigm of multiple-objective EAs (which often address only two or three objectives) has advanced to many-objective EAs (which are intended to address four or more objectives that may be in conflict with each other in many cases). Therefore, this article considers many objectives of the sensor selection problem in the IoT system, including optimization of communication energy consumption, energy balancing on all devices, energy harvesting, green concerns, and QoS. The problem is resolved by a tailored many-objective EA based on decomposition to increase computational efficiency and solution quality. By simulation, the proposed EA is shown to be promising through scatter charts and parallel coordinates.