© 2020 ASTES Publishers. All rights reserved.Traditional networks have difficulty in meeting the technological developments and the continuous increase in the size of data to be processed. Software-Defined Network (SDN) approach has emerged as an alternative to traditional networks. SDN separates the control and data planes from each other and manages the network over the control plane with flexibility and cost advantages. In networks with large data flow, SDN with multiple controllers will be useful to manage the network because a single controller may cause network interruptions and data loss. After deciding on the use of multiple controllers in the SDN approach, problems are encountered with the number of controllers that should be used and the placement of these controllers. Due to the increase in the coronavirus epidemic that has affected the whole world in recent months, the need for pandemic hospitals has increased. However, considering the establishment costs of pandemic hospitals, it will not be possible to establish these hospitals in every desired location in the impact area. For this reason, positioning pandemic hospitals at more strategic points will provide these hospitals with a wider coverage area and more functionality at lower costs. In this paper, a genetic algorithm-based SDN is presented for pandemic hospital localization. The number of coronavirus infected people of each city in Turkey is taken into consideration as well as the city distances in the ULAKNET data set within the Topology Zoo database. For the best localization of pandemic hospitals, the Dijkstra algorithm is used to best cover the cities where the coronavirus epidemic is at a minimum distance from the cities. In this paper, the number of controllers is set as 10, and the experimental results are given with maps and graphics.