Evolutionary algorithms (EAs) have been recognized as a promising approach for bi-level optimization. However, the population-based characteristic of EAs largely influences their efficiency and effectiveness due to the nested structure of the two levels of optimization problems. In this paper, we propose a transfer learning based parallel evolutionary algorithm (TLEA) framework for bi-level optimization. In this framework, the task of optimizing a set of lower level problems parameterized by upper level variables is conducted in a parallel manner. In the meanwhile, a transfer learning strategy is developed to improve the effectiveness of each lower level search (LLS) process. In practice, we implement two versions of the TLEA: the first version uses the covariance matrix adaptation evolutionary strategy and the second version uses the differential evolution as the evolutionary operator in lower level optimization. Experimental studies on two sets of widely used bi-level optimization benchmark problems are conducted, and the performance of the two TLEA implementations is compared to that of four well-established evolutionary bi-level optimization algorithms to verify the effectiveness and efficiency of the proposed algorithm framework.