Dynamic Stochastic General Equilibrium (DSGE) models are flawed, as became clear after the 2007-2008 financial crisis. Efforts to subdue the shortcomings have been insufficient: to this date, there is still a demand for building a new framework to study policy implications and make decisions. We propose a novel monetary policy strategy, in an attempt to provide an auxiliary tool to central banks, whose main predictive models are still from the DSGE family.We derive an objective function from three empirical relationships that have long been established in economic literature: Okun s Law, the Phillips Curve, and liquidity effects. Using data from Brazil, we seek to minimise the value of this function by choosing the interest rate via a genetic algorithm. Since the function is forward looking, we use a neural network to predict values of unemployment and inflation. Results suggest that had the Brazilian central bank applied our strategy, and all other economic conditions remained identical, inflation could have been lower for 62.48 percent of the time. Predicted unemployment, however, was lower only for 39.69 percent of covered periods, as it faces a trade-off with inflation. We discuss the applicability of the proposed strategy and argue for its theoretical soundness.