The novel coronavirus is one of the most widespread global epidemics that has harmed human life and health around the world in the past century. Nonetheless, we confront a formidable predicament: the transportation costs and transmission risks of comprehensive testing for the new coronavirus are huge. Furthermore, the extensive utilization of trucks has contributed to a surge in greenhouse gas emissions, thereby exacerbating the pressing issues of global warming and climate change. To ameliorate these challenges, we proffer an ingenious resolution: the deployment of a truck outfitted multiple drones, thereby orchestrating the transfer of samples. In our endeavor to address this intricate issue, we have devised a MIP model, with distribution cost and delivery time serving as bi-objectives. Additionally, we have developed a genetic adaptive large-scale neighborhood search algorithm (GALNS) to resolve the model. Through example testing, we draw the following conclusions: a. We verified the correctness and effectiveness of the proposed model and algorithm. b. In comparison to traditional truck transfer, the deployment of trucks ferrying multiple drones, each equipped with multi-visit functionalities, for the transportation of nucleic acid testing samples, not only proves to be a more cost-effective and efficient approach but also mitigates the risk of contagion.