Economic success of an IOR project primarily depends on the effectiveness of the selected method in the reservoir of interest and the way it is deployed. Due to the heavy computational work required for a large number of simulations, it is rather an arduous task to determine optimum design schemes for a given project. Proxy models that mimic reservoir models can potentially offer an opportunity to reduce the number of simulation runs and allow timely reservoir-management decisions to be made. In this study, a high-performance screening/optimization workflow is presented to narrow the ranges of possible scenarios to be modeled using conventional simulation. The workflow proposed consists of three fundamental steps: 1. Creating a knowledge base with simulations for a wide range reservoir characteristics and design scenarios 2. Training neural-network based proxy models with the knowledge base generated in Step 1 3. Using the genetic algorithm to search for the optimum design scenario by evaluating the objective function via proxy models This workflow is applied to the cyclic pressure pulsing process with CO2 and N2 in naturally-fractured reservoirs. Developed proxies are universal such that the range of their applicabilities is extended to a wide spectrum of reservoir characteristics, including initial conditions, well spacing and reservoir fluid types (heavy, black, and volatile). Proxy models proved to be able to predict critical performance indicators such as cumulative oil production, and oil flow rates within high levels of accuracy. The genetic algorithm is coupled with proxy models to maximize the discounted oil volume produced per injected volume for a specified period of operation. After utilizing the aforementioned workflow, it is possible to significantly reduce the computational time and manpower expended with an initial screening of the cyclic pressure pulsing process. In this way, more effective design strategies can be structured for the reservoir under consideration.