The design of a transformer must meet minimum requirements of efficiency and regulation, maximizing the power to be transferred per unit of mass or volume, and supports a well defined maximum elevation of temperature. Conflicting objectives lead to the employment of optimization methods in the transformer design. Multiobjective optimization problems (MOPs) consist of several competing and incommensurable objective functions. Recently, as a search and optimization technique inspired by nature, evolutionary algorithms (EAs) have been broadly applied to solve MOPs. Various EAs have been proposed for this purpose, and their usefulness has been demonstrated in several application domains of science and engineering. In this paper, we propose the unrestricted population-size evolutionary multiobjective optimization algorithm (UPS-EMOA) approach combined with chaotic sequences (CMOA). Our approach integrates the merits of both UPS-EMOA and chaotic sequences to improve the efficiency of the optimization procedure. Numerical results of transformer design optimization demonstrate the effectiveness of the proposed CMOA when compared with the UPS-EMOA approach to preserve the diversity of the solutions and find nondominated solutions.