Objectives: Our work presents a data mining approach, using Krill Herd optimization algorithm to generate more comprehensive classification of breast cancer dataset. Hybrid Krill Herd algorithm (HKH) is proposed which is suitable for generation of optimized classification rules. Methods: HKH algorithm has a very flexible encoding where each member of population (Krill Herd) corresponds to a classification rule. Each Krill Herd consist of many krills corresponding to antecedents of classification rule. For breast cancer classification, Wisconsin diagnostic breast cancer data set created from fine needle aspiration biopsy of breast mass was used. Findings: Using this optimization algorithm a very comprehensive and simple classification rule is obtained for breast cancer classification represented in form of If-Then rule. Application: The obtained rule can be used to further classify breast tissue specimens into malignant and benign classes, thus supporting the cancer diagnosis.