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MLP which is one of the most commonly used classifier, is a feed-forward, supervised neural network topology. Back propagation algorithm is used for minimizing the error between network output and the target value. According to classification process, MLP structure and learning parameters, which are used in back propagation algorithm, are needed to decide for increasing the test accuracy. Commonly these variables are chosen randomly, so finding the values that give maximum test accuracy is a timeconsuming process. In this paper, learning parameter of back propagation algorithm and network structure are optimized to success a faster and efficient weight-update process by using three different heuristic optimization algorithm, ABC, GA and SA. Both of the used two datasets contain human activity sensor data. For two datasets, three algorithms are compared and detailed test results are given. It is observed that, although SA is the fastest one among chosen algorithms, ABC shows the highest performance for test accuracy of MLP classifier.