Several problems are encountered when measuring the flatness error of large and complex workpieces in a production line, such as abroad area of the detection surface and a vast amount of data. To improve the efficiency and accuracy of flatness error detection, an optimization algorithm was adopted to increase the speed of flatness error evaluation. The Differential Evolution (DE)algorithm was implemented for solving these problems, and the optimization method of Particle Swarm Optimization(PSO) algorithm was integrated into the DE algorithm framework to increase the convergence speed by improving the mutation operation. This study proposed a mathematical model using the minimum zone method for the flatness error evaluation of large workpieces and expounded the principle and implementation steps of the improved DE algorithm. Finally, using the outer panel of a forklift truck as an example, the convergence speed and accuracy of the algorithm were verified by evaluating the flatness error of the outer panel. The results demonstrate that the convergence result of the improved DE algorithm is stable for evaluating the flatness error of large workpieces, and the error is close to zero. The accuracy of the proposed algorithm is 36.83% higher than that of the genetic algorithm, and the convergence speed is 58.33% and 28.57% higher than those of the genetic algorithm and standard DE algorithm, respectively. The proposed algorithm can be satisfactorily applied to the flatness error detection of large workpieces to improve the detection efficiency.