In this paper, we apply simulation-based optimization methods to detect quadrature amplitude modulated (QAM) MIMO signals. The first one is the so-called Cross-Entropy (CE) method which enables the corresponding CE-based detector to provide bit-error-rate (BER) performance close to that achievable by the maximum-likelihood (ML) detector when the signal-to-noise ratio (SNR) is relatively low. Unfortunately, the performance curves exhibit error floors in the high SNR region. To improve the performance in the high SNR region, we borrow the concept of Particle Swarm Optimization (PSO) and refer to the resulting iterative detector as the Particle-Swarm-driven Cross-Entropy (PSD-CE) MIMO detector. This detector gives a significant BER performance improvement in the medium-to-high SNR region. We also consider the case when the channel state information is imperfect and suggest a robust detector structure based on a modified score function.