In this paper, an automatic differentiation-based sequential quadratic programming (AD-SQP) method is proposed for parameter estimation of nonlinear growth models for industrial crystallization processes. Based on the process population balance equation (PBE) related to the size-dependent growth kinetic, the parameter estimation problem is transformed into a constrained optimization problem, such that an automatic differentiation (AD) algorithm is established to efficiently compute the gradients of the model fitting error for parameter estimation. A comparative study between SQP and AD-SQP is made for reference. Simulation results demonstrates that the proposed method is more efficient and accurate than the traditional SQP. Experiments on the seeded batch cooling crystallization of the β form L-glutamic acid (β-LGA) with different temperature cooling profiles are performed to apply the proposed AD-SQP to identify the growth model. The predicted moments of crystal size distribution (CSD) and solution concentration well match with the measured results.