In this paper, an integrated technique has been developed to evaluate and optimize performance of hybrid steam-solvent processes in a post-cold heavy oil production with sand (CHOPS) reservoir with consideration of wormhole networks. A reservoir geological model is developed and calibrated by history matching reservoir pressure with oil, gas, and water production rates as the input constraints, while its wormhole network is characterized with a newly developed pressure-gradient-based (PGB) sand failure criterion conditioned to sand production. Once calibrated, the reservoir geological model incorporated with the wormhole network is then employed to evaluate and optimize performance of hybrid steam-solvent processes under various conditions, during which the net present value (NPV) is maximized with an integrated optimization algorithm by taking injection time, soaking time, production time, and injected fluid composition as controlling variables. It is found that a huff-n-puff process imposes a positive impact on enhancing oil recovery when wormhole network is fully generated and propagated. Addition of alkane solvents into CO2 stream leads to a higher oil recovery compared with that of the CO2 only method, while all hybrid steam-solvent injection achieve high oil recovery by taking advantage of both thermal energy and solvent dissolution. It is found that the NPV reaches its maximum value when the steam temperature is 200 °C for the optimized hybrid steam-solvent scenario.