In this paper, we propose a scalable algorithm for solving resource allocation problems on large datasets. This class of problems is posed as a multi-objective optimization problem in a Maximum Entropy Principle framework. This algorithm solves a multi-objective optimization problem that minimizes simultaneously the coverage cost and the computational cost by appropriate recursive prescription of smaller subsets required for a 'divide and conquer' strategy. It provides characterization of the inherent trade-off between reduction in computation time and the coverage cost. Simulations are presented that show significant improvements in the computational time required for solving the coverage problem while maintaining the coverage costs within prespecified tolerance limits.