A hybrid Lagrange interpolation differential evolution algorithm (HLIDE) is proposed in this study for path synthesis of four-bar mechanisms. HLIDE combines two algorithms of differential evolution (DE) with Lagrange interpolation local search (LILS). LILS performs a local search in the neighborhood of the best individual to enhance the local exploitation capability of DE. In addition, an adaptive local search strategy is presented to further improve the efficiency of LILS. This technique can adaptively apply LILS based on the performance of LILS and DE in previous generations. To evaluate the efficiency and accuracy of HLIDE, five cases of path synthesis involving four-bar mechanisms were tested. Moreover, three well-known evolutionary algorithms, i.e., particle swarm optimization, teaching-learning-based optimization, and differential evolution, were implemented and compared in the five cases. Previous solutions for the same path generation problems by different evolutionary algorithms are summarized and compared in this study. Experimental results showed that HLIDE has significantly better performance in solving the synthesis problems of mechanisms when compared to other algorithms.