Purpose: A major component in managing pandemic outbreaks involves testing the suspected individuals and isolating them to avoid transmission in the community. This requires setting up testing centres for diagnosis of the infected individuals, which usually involves movement of either patient from their residence to the testing centre or personnel visiting the patient, thus aggregating the risk of transmission to localities and testing centres. The purpose of this paper is to investigate and minimize such movements by developing a drone assisted sample collection and diagnostic system. Design/methodology/approach: Effective control of an epidemic outbreak calls for a rapid response and involves testing suspected individuals and isolating them to avoid transmission in the community. This paper presents the problem in a two-phase manner by locating sample collection centres while assigning neighbourhoods to these collection centres and thereafter, assigning collection centres to nearest testing centres. To solve the mathematical model, this study develops a mixed-integer linear programming model and propose an integrated genetic algorithm with a local search-based approach (GA-LS) to solve the problem. Findings: Proposed approach is demonstrated as a case problem in an Indian urban city named Kolkata. Computational results show that the integrated GA-LS approach is capable of producing good quality solutions within a short span of time, which aids to the practicality in the circumstance of a pandemic. Social implications: The COVID-19 pandemic has shown that the large-scale outbreak of a transmissible disease may require a restriction of movement to take control of the exponential transmission. This paper proposes a system for the location of clinical sample collection centres in such a way that drones can be used for the transportation of samples from the neighbourhood to the testing centres. Originality/value: Epidemic outbreaks have been a reason behind a major number of deaths across the world. The present study addresses the critical issue of identifying locations of temporary sample collection centres for drone assisted testing in major cities, which is by its nature unique and has not been considered by any other previous literature. The findings of this study will be of particular interest to the policy-makers to build a more robust epidemic resistance.