A multiobjective optimization was performed to maximize native protein concentration and shelf life of ASD, using artificial neural network (ANN) and genetic algorithm (GA). Optimum pH, storage temperature, concentration of protein, and protein stabilizers (Glycerol, NaCl) were determined satisfying the twin objective: maximum relative area of the dimer peak (native state) after 48 h of storage, and maximum shelf life. The relative area of the dimer peak, obtained from size exclusion chromatography performed as per the central composite design (CCD), and shelf life (obtained as turbidity change) served as training targets for the ANN. The ANN was used to establish mathematical relationship between the inputs and targets (from CCD). GA was then used to optimize the above determinants of aggregation, maximizing the twin objectives of the network. An almost fourfold increase in shelf life (similar to 196 h) was observed at the GA-predicted optimum (protein concentration: 6.49 mg/ml, storage temperature: 20.8 degrees C, Glycerol: 10.02%, NaCl: 51.65 mM and pH: 8.2). Since no aggregation was observed at the optimum till 48 h, all the protein was found at the dimer position with maximum relative area (64.49). Predictions of the finally adapted network also reveal that storage temperature and solvent glycerol concentration plays key role in deciding the degree of ASD aggregation. This multiobjective optimization strategy was also successfully applied in minimizing the batch culture period and determining optimum combination of medium components required for most economical production of actinomycin D.