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Community Supported Agriculture (CSA), which offers two outstanding advantages, high-quality food and localized production, has come to the fore. In CSA, the output of picking scheduling is the input of delivery scheduling. Hence, only by scheduling the picking stage and distribution stage in a coordinated way can we achieve fresh agricultural products at minimum cost. However, due to asymmetric information in the picking and distribution stage, the integrated scheduling of picking and distribution may lead to an asymmetric optimization problem, which is suitable for solving with an iterative algorithm. Based on this, this work studies an integrated scheduling problem of the picking and distribution of fresh agricultural products with the consideration of minimizing picking and distribution costs as well as maximizing the freshness of orders. First, a nonlinear mixed-integer programming model for the problem under consideration is constructed. Second, a multi-objective multi-population genetic algorithm with local search (MOPGA-LS) is designed. Finally, the algorithm is compared with three multi-objective optimization algorithms in the literature: the non-dominated sorted genetic algorithm-II (NSGA-Ⅱ), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the multi-objective evolutionary algorithm based on decomposition that is combined with the bee algorithm (MOEA/D-BA). The comparison results show the excellent performance of the designed algorithm. Thus, the reported model and algorithm can assist managers and engineers in making well-informed decisions in managing the farm operation. [ABSTRACT FROM AUTHOR]