The construction industry is undergoing a significant transformation with the emergence of 3D concrete printing (3DCP), a technology that has the potential to revolutionize the construction process. Additive manufacturing (AM), specifically 3D concrete printing, offers the advantages of enhanced customization, reduced waste, and accelerated construction timelines. One crucial aspect of 3D concrete printing is toolpath planning, which involves determining the optimal route for the printer's extruder nozzle. However, despite the advancements in 3DCP, the process is not entirely automated, as labor is still required for supporting activities. Examples of supporting activities include the installation of temporary formwork for openings, overhangs, or MEP ducts and fixtures. These additional tasks can lead to multiple interruptions affecting the quality and productivity of the printing process. While existing literature on 3DCP primarily focuses on material properties and concrete rheology, there is a lack of research on the impact of supporting activities and resource limitations on the manufacturing of 3D printed concrete buildings. To address this gap, this paper introduces a novel optimization heuristic algorithm for the toolpath planning and task scheduling of the 3D concrete printing process, along with a BIM-based data extraction and slicing algorithm. The simulation model considers an XYZ gantry printer system and incorporates the influence of supporting activities and human resource requirements. Furthermore, variations in material composition are accounted for by integrating the rheological properties of the 3D printing concrete. A parameter variation study is conducted to measure the effect of the process parameters on the volumetric production rate.