A time has arrived in the field of robotics, where autonomous robots are likely to help humans in activities such as emergency services, particularly in response to fires and/or earthquakes as well as floor cleaning, mining, search and rescue etc. and all of these problems are related to coverage. The aim is to complete the scanning of the environment efficiently in a reasonable amount of time. In known/unknown environments, using multiple robots/drones typically allows less time to be needed to complete the scanning task than when using a single robot. However, the search performance can be poor in large, complex environments. Therefore, partitioning a terrain is important in order to effectively distribute the robot search work so that good coverage can be achieved in a reasonable amount of time. In this paper a novel Square Based Terrain Partitioning (SBTP) algorithm is presented using a genetic algorithm to partition a known environment into multiple domains in a multi-robot exploration system. In addition, a second genetic algorithm is presented to allocate the domain search workload in such a way that, given a certain number of drones, the overall search time is minimized.