This paper presents a novel multi-robot coverage path planning (CPP)\nalgorithm - aka SCoPP - that provides a time-efficient solution, with workload\nbalanced plans for each robot in a multi-robot system, based on their initial\nstates. This algorithm accounts for discontinuities (e.g., no-fly zones) in a\nspecified area of interest, and provides an optimized ordered list of\nway-points per robot using a discrete, computationally efficient, nearest\nneighbor path planning algorithm. This algorithm involves five main stages,\nwhich include the transformation of the user's input as a set of vertices in\ngeographical coordinates, discretization, load-balanced partitioning,\nauctioning of conflict cells in a discretized space, and a path planning\nprocedure. To evaluate the effectiveness of the primary algorithm, a\nmulti-unmanned aerial vehicle (UAV) post-flood assessment application is\nconsidered, and the performance of the algorithm is tested on three test maps\nof varying sizes. Additionally, our method is compared with a state-of-the-art\nmethod created by Guasella et al. Further analyses on scalability and\ncomputational time of SCoPP are conducted. The results show that SCoPP is\nsuperior in terms of mission completion time; its computing time is found to be\nunder 2 mins for a large map covered by a 150-robot team, thereby demonstrating\nits computationally scalability.\n