Due to the explosion in restructuring of power markets within a deregulated economy, competitive power market needs to minimize their required generation reserve gaps. Efficient load forecasting for future demands can minimize the gap which will help in economic power generation, power operations, power construction planning and power distribution. Nowadays, neural networks are widely used for solving load forecasting problem due to its non-linear characteristics. Consequently, neural network is successfully combined with optimization techniques for finding optimal network parameters in order to reduce the forecasting error. In this paper, firstly a novel evolutionary algorithm based on follow the leader concept is developed and thereafter its performance is validated by COmparing Continuous Optimizers experimental framework on the set of 24 Black-Box Optimization Benchmarking functions with 12 state-of-art algorithms in 2-D, 3-D, 5-D, 10-D, and 20-D. The proposed algorithm outperformed all state-of-art algorithms in 20-D and ranked second in other dimensions. Further, the proposed algorithm is integrated with neural network for the proper tuning of network parameters to solve the real world problem of short term load forecasting. Through experiments on three real-world electricity load data sets namely New Pool England, New South Wales and Electric Reliability Council of Texas, we compared our proposed hybrid approach to baseline approaches and demonstrated its effectiveness in terms of predictive accuracy measures.