The differential evolution (DE) algorithm is a simple but powerful population-based stochastic search technique for solving global optimization problems. DE consists of three main operations: mutation, crossover and selection. The advantages of DE are simple structure, ease of use, speed and robustness. However, to achieve optimal performance with DE, time consuming parameter tuning is essential as its performance is sensitive to the choice of the mutation and crossover values. In this paper, a novel DE algorithm (NDE) based on truncated gamma probability distribution function is proposed for solving multiobjective optimization problems as the design of transformers. Simulations of transformer design optimization (TDO) demonstrate the effectiveness of the proposed optimization algorithm. The simulation results show that, compared with other multiobjective DE algorithm, the proposed NDE is able to find better spread of solutions with better convergence to the Pareto front and preserve the diversity of Pareto solutions more efficiently.