This document proposes a new optimal subsampling strategy for logistic regression models based on D-optimal designs. The algorithm iteratively takes subsamples of increasing size based on the current parameter estimates. It selects data points that maximize the determinant of the information matrix to better preserve information from the full dataset. Simulation results show the new algorithm outperforms random sampling and existing subsampling methods, achieving lower mean squared errors for parameter estimates, especially in small sample size scenarios. Ongoing work looks to incorporate additional modeling improvements and applications.