Extreme Learning Machine (ELM) is a simple learning algorithm for singlehidden-layer feed-forward neural network. The learning speed of ELM can be thousands of times faster than back-propagation algorithm, while obtaining better generalization performance. However, ELM may need high number of hidden neurons and lead to ill-condition problem due to the random determination of the input weights and hidden biases. In order to surmount the weakness of ELM, this paper proposes an optimization scheme for ELM based on artificial chemical reaction optimization algorithm (ACROA). By using ACROA to optimize the hidden biases and input weights according to both Root mean squared error and the Norm of output weights, the classification performance of ELM will be improved. The experimental result on several real benchmark problems demonstrates that the proposed method can attain higher classification accuracy than traditional ELM and other evolutionary ELMs.