Accounting for aeroelastic phenomena, such as flutter, in the conceptual design phase is becoming more important as the trend toward increasing the wing aspect ratio forges ahead. However, this task is computationally expensive, especially when utilizing high-fidelity simulations and numerical optimization. Thus, the development of efficient computational strategies is necessary. With this goal in mind, this work proposes a surrogate-based optimization (SBO) methodology for wing design using a predefined machine learning model. For this purpose, a custom-made Python framework was built based on different open-source codes. The test subject was the classical Goland wing, parameterized to allow for SBO. The process consists of employing a Latin Hypercube Sampling plan and subsequently simulating the resulting wing on SHARPy to generate a dataset. A regression-based machine learning model is then used to build surrogate models for lift and drag coefficients, structural mass, and flutter speed. Finally, after validating the surrogate model, a multi-objective optimization problem aiming to maximize the lift-to-drag ratio and minimize the structural mass is solved through NSGA-II, considering a flutter constraint. This SBO methodology was successfully tested, reaching reductions of three orders of magnitude in the optimization computational time.