Optimizing multistage fractured horizontal wells (MFHW) can tap the full potential of tight oil reservoirs. Although recent studies have introduced various frameworks, most of the significant parameters for MFHW are not optimized simultaneously, which may lead to actual performance that is far below expected performance, especially in heterogeneous reservoirs. Here, we present an efficient optimization framework that couples embedded discrete fracture model (EDFM) and intelligent algorithms to maximize net present value. We also examined the performance of four optimization algorithms in our model: genetic algorithm (GA), multilevel coordinate search (MCS), covariance matrix adaptation evolution strategy (CMA-ES), and generalized pattern search (GPS). Our results suggest that because CMA-ES handles MFHW optimization robustly and effectively, it may be utilized in future applications. Our framework serves as an efficient tool to optimize MFHW design, which plays an increasingly significant role in the enhancement of tight oil recovery. (C) 2018 Elsevier Ltd. All rights reserved.