As a combination of MIMO and OFDM systems, multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system has high band utilization and can effectively combat multipath effects in wireless channels. In the paper, we studies sparse channel estimation and pilot optimization problems for MIMO-OFDM systems. The channel estimation problem in MIMO-OFDM systems is transformed into the sparse signal reconstruction problem in compressed sensing (CS) theory. The pilot optimization is based on minimizing the mutual coherence of the measurement matrix. In combination with existing stochastic sequential search (SSS) and extension scheme 2(ES2) algorithms and pilot shift mechanism, a fast pilot optimization algorithm stochastic sequential search-shift mechanism (SSS-SM) is proposed. The algorithm has lower computational complexity, and the computation time is not affected by the number of transmit antennas. The pilot design results obtained by SSS-SM algorithm and ES2 algorithm are applied to the channel estimation of MIMO-OFDM system. Simulation results show that SSS-SM can achieve the same channel estimation performance as ES2 with less computational complexity. In the case of high signal-to-noise ratio (SNR), the mean square error (MSE) of SSS-SM is about averaged 3 dB lower than that of ES2, which shows that the method has advantages over high SNR.