Order picking is the process of collecting products from the warehouse to fulfill specific customer orders, which is known to be labor- and time-intensive. Order picking significantly determines the warehouse's performance, contributing 50% to 75% of operational costs. Therefore, the efficiency of the order-picking process also affects the supply chain performance. This study investigates two of five main order-picking problems jointly, which are classified in its day-to-day operational problem: order batching and picker routing. The algorithm to solve the problem is developed using metaheuristics, the simulated annealing algorithm. The algorithm avoids local optimization to find better solutions. The algorithm is validated through preliminary testing against problem instances of online grocery shopping cases with 1560 products where each order can consist of many items. The proposed algorithm has proven to be more efficient than the previous approach, which gives quality solutions using less run-time, especially for larger problem instances.