In this work, a multi-step process using GAs is used to optimize material selection for tandem OSCs. The first step is to predict the PCE of a donor/NFA pair. In our previous work, a data-driven model trained on approximately 110 polymer donor/NFA pairs was proposed to efficiently predict PCE . Since then, a more extensive literature search has been carried out to expand the data set to 1,225 donor/NFA pairs, of which 1,001 are unique pairs, with PCE ranging from 0.01% to 18.77%. This data set includes electronic properties and photovoltaic performance parameters. The reported experimental PCEs were used as a training set to develop an updated computational prediction model. Using semi-empirical methods, chemical properties are obtained for each material. These properties, such as band gaps and oscillator strengths can affect device performance and are used as input descriptors for developing a model. Eight machine learning models were examined, with an ensemble of random forest and a neural network showing the best performance for prediction of PCE. This ensemble model, hereby referred to as Organic Photovoltaic Efficiency Predictor 2 (OPEP2), is trained on donor/NFA pairs with an experimental PCE greater than 10%. Due to its good predictive power on high-performing pairs, it is used as the fitness function in the first two sets of GAs to discover new NFAs and donors. The first set of GAs explored various types of NFAs, such as fused and unfused backbones, paired with the 214 donors from the OPEP2 dataset. The top 200 NFAs were used in the second step for designing compatible polymer donors using a GA. In the last stage, the top three performing pairs were used as target cells for three GAs. These GAs were designed to find high-performance materials for the other subcell in a tandem device. Since there is little experimental data on tandem OSCs to train on, the GA scored on the basis of maximizing solar optical absorption, while minimizing spectral overlap. The resulting tandem cells show broad absorption and high PCE for each sub-cell. The new proposed materials show interesting design strategies such as 3-D end groups and diphenylamine substituents on the core. The incorporation of different classes of acceptors, such as the Y-series and fused backbones, can lead to broader absorption.