The space–air–ground integrated network (SAGIN)-related issues are attractive in the sixth generation (6G) technologies, which facilitate the global coverage and seamless service. The cooperation of high altitude platforms (HAPs) and low-Earth orbit (LEO) satellites provides the remote area users with comprehensive coverage and service. In this work, we consider the cooperation of HAPs and LEO satellites in SAGIN to serve terrestrial users in the remote area for data collection and transmission. To deal with the periodical motion of LEO satellites, we employ the time expanding graph (TEG) to represent the multiple resources in SAGIN and depict task flow transmission processes. Based on TEG, we aim to maximize the total data received by the ground data processing center in a time horizon, considering multiple resource constraints and flow restrictions. The original problem is formulated in the form of mixed-integer linear programming, which is intractable to obtain the optimal solution by brute-force searching. To alleviate this intractability, we propose the Benders decomposition-based algorithm to obtain the optimal solution within an acceptable time complexity via iterations between the master problem and subproblem. Moreover, to further expedite the solution in large-scale systems, an acceleration algorithm is proposed by handling the master problem with an approximation algorithm and the subproblem with a unit-flow-based algorithm. Finally, simulations are conducted and the numerical results verify the efficiency of the proposed algorithms, and the effects of various network parameters are analyzed as well.