The challenge of the Team Formation Problem (TFP) is to select an effective team for a given task with minimum communication cost and time. The heuristic as well as the recent evolutionary approaches to team formation focus on optimizing one communication cost, yet do not yield teams in a reasonable amount of time. Our study proposes a novel approach to solve the TFP by considering the topology of the underlying social network. We propose two heuristic algorithms: TPLRandom and TPLClosest, that use the degree distribution of the social network to solve TFP. The proposed approach utilizes the power law followed by the degree distribution as well as the skill distribution of the experts to compose an effective team. This idea optimizes not only more than one communication cost but also time. Extensive experimentation is carried out on the large well-known real-world DBLP dataset and all the subnetworks of DBLP. Empirically it is observed that the proposed algorithm TPLClosest is 3, 10, 27, and 65 times faster than Genetic algorithm, MinSD, Cultural algorithm and MinLD algorithm respectively. Further our algorithms are found to be significantly scalable. The results are shown empirically on different sized networks of DBLP.