In typical combinatorial problems such as Travelling Salesman problem (TSP) or Knapsack problems, it is challenging to calculate the optimal configuration of solutions using brute-force search due to their high computational complexity. Therefore, while dealing with many such problems it is common to employ techniques that prune the obvious inefficient solutions and reduce the search space for the agents involved. In this paper, we address a drone path-planning problem, aimed at surveying the maximum area of a given field within the battery constraints of individual drones. The methodology devised in this article involves a combination of widely recognized algorithms such as backtracking and the greedy algorithm along with a socio-inspired optimization algorithm: Cohort Intelligence. The proposed architecture bases itself on aforementioned algorithms as well as a reward optimization technique that incentivizes of unvisited areas of the field.