<p>The magnesium alloys exhibit great potential as a biodegradable orthopedic implant due to its comparable density, strong mechanical properties, and non-toxic nature in bodily fluids. But the rate of magnesium alloy deterioration is extremely high in aquas environments. It is difficult to regulate the rate of corrosion in an aquatic environment while still preserving the appropriate mechanical strength. In this work, informatics-based approach is used to determine the composition of various alloying elements in order to optimize mechanical properties and corrosion rate. The data-driven models are developed using bi-objective genetic programming, a machine learning tool, and the composition of the magnesium alloy is determined using a multi-objective genetic algorithm to yield the best possible results in terms of mechanical properties and corrosion rate, using the developed models as the objective functions. It is found that high amount of Zr, Y and Ce are preferred, in addition to Zn and Al, for the Mg alloy. The importance of Ca and Sr is found to be more for controlling the corrosion rate, rather than the mechanical properties. Al, reported of having adverse effect on human body, can be completely avoided. One of the alloy compositions in the Pareto front is developed experimentally. The mechanical and electrochemical tests show encouraging results.</p>