Brushless DC motors (BLDCM) are widely used in the industrial field due to their high power density and good speed regulation performance. Optimization design of a BLDCM requires consideration of multiple objectives such as torque, mass, efficiency and etc., which makes it a multi-objective optimization (MOO) problem. In this paper, a novel MOO algorithm based on Sparrow Search Algorithm (SSA) is proposed. Firstly, a Pareto solution selection mechanism is put forward, which transforms the SSA, originally a single objective optimization algorithm, into a MOO algorithm, then to address the issue of traditional SSA being prone to local optima, Latin hypercube sampling and opposition-based learning strategies are introduced in the population initialization process, and Gaussian mutation is later used to modify the population update mechanism of SSA. Performance of the proposed algorithm is verified using ZDT series test functions. Finally, it is applied to an optimal design problem of a typical BLDCM. Numerical results demonstrate the correctness and effectiveness of the proposed algorithm.