Accurate assessment of the Ki-67 proliferation index is essential in histopathology, yet manual counting of positively stained nuclei in immunohistochemistry slides is time-consuming and subject to inter-observer variability. This paper presents an automated method for Ki-67 index estimation based on morphological image processing and evolutionary optimization. The approach integrates color-based preprocessing, morphological filtering, and distance transform–based cell separation to segment and quantify stained nuclei. A genetic algorithm is used to optimize key parameters to improve segmentation robustness across heterogeneous tissue samples. Experimental results indicate that parameter optimization enhances consistency compared to non-optimized configurations. The proposed method provides an automated alternative to manual assessment and supports label generation for deep learning applications in digital pathology.