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Vertical farming (VF) allows for more plants to be cultivated in a particular land area, maximizing land-use efficiency. VF employs multileveled shelves enabling crops to be planted at each level. This paper aims to develop an optimization model that could generate a required setup of a vertical shelf that would meet the sunlight requirements of strawberries. Firstly, the simulation model was developed using Rhino (Rhinoceros) software with Grasshopper plugin, which can predict the sunlight availability at each VF shelf level. The model is validated by the experiment in Chiang Mai province, Thailand, at an elevation higher than 310 m above mean sea level. The experiment set up of the VF shelf with three levels was constructed, and the solar radiation values were recorded and validated against the radiation predicted by the model. The validated model is used to simulate the sunlight radiation on each level of the shelf for different shelf designs with varying vertical spacings between each level. These sets of predicted radiation values and vertical spacings are used to train the ANN (Artificial Neural Network) model. The ANN simulation model was developed using MATLAB software with Simulink and Neural Net Fitting App. The trained ANN model has an MSE (Mean Squared Error) of 0.00019621 and an R (the correlation coefficient) value of 0.9992. As a result, the optimization model could successfully give out the vertical spacing between each level of the VF shelf with an input of desired sunlight availability on each level. For the PPFD (Photosynthetic Photon Flux Density) of 600 µmol/m2/s on both levels, the spacing should be 51.34 cm from the top to the middle (y1) and 48.69 cm from the middle to the bottom (y2). To receive 600 and 500 µmol/m2/s on the middle and bottom level respectively, the levels should be placed 66.77 cm (y1) and 30.89 cm (y2) apart. [ABSTRACT FROM AUTHOR]