Short-term forecasting plays an important role in the economic area. Several studies have been carried, where models with good forecast capacity, focusing on accuracy or stability, were built. Modeling only one of these characteristics without the other can lead to a model with lower generalization capacity. To deal with such situation, this study proposes an ensemble model (EM) to forecast one, two and three months ahead the 60 kg corn bag prices received by producers in the state of Parana (Brazil). Additionally, feature extraction by means of principal component analysis is employed. The EM is built using machine learning models as base (weak) learners (BL) combined by weighted sum. The adopted BL are: Extremely randomized trees, partial least squares, k-nearest neighbors, neural network, bagging and multivariate adaptive regression splines. The weights are chosen through multi-objective optimization, where bias and variance are minimized. The multi-objective differential evolution with spherical pruning algorithm is adopted, while physical programming is used in order to obtain the preferred set of weights. The model built is appointed as multi-objective ensemble model (MOEM). The performance of the model is evaluated using mean absolute percentage error, mean squared error and root mean squared error. Additionally, the Diebold-Mariano test is used to evaluate the reduction on forecasting errors. In general lines, the results show that forecasting using MOEM with two, three or four BL is more stable and accurate than forecasting with single BL. Therefore, this approach is recommended to make short-term forecast of corn prices, which leads to a more assertive decision making.