In this research, a grid search is employed to find the optimal parameter and an optimized K-Nearest Neighbor (KNN) based breast cancer detection model is proposed. The grid search is used to find the best combinations of parameters that could produce better breast cancer detection accuracy. Moreover, this study explored the effect of parameter tuning on the performance of KNN algorithm foe breast cancer detection. The findings of this research reveals that parameter tuning has a significant effect on the performance of the proposed model. The effect of parameter tuning on the performance of KNN algorithm is experimentally tested using Wisconsin breast cancer dataset collected from kaggle data repository. Finally, we have compared the performance of the KNN algorithm with the tuned hyper-parameter and with default hyper-parameter. The result analysis on the performance of the KNN algorithm on breast cancer detection on the test dataset reveals that the accuracy of the proposed optimized model is 94.35% and the performance of the KNN algorithm with the default hyper-parameter is 90.10%.