In the last few years, the automotive industry has reinvented itself to meet the demands of the international market, which has been increasingly competitive in a context with environmental laws each year more severe. One alternative to lower harmful greenhouse gases emissions over the life of the vehicle is electric cars. However, the production and disposal of electric batteries is still a major problem to be solved. Therefore, companies are also searching for other potentialities to increase the internal combustion engine s efficiency and develop green technology, such as Homogeneous Charge Compression Ignition (HCCI) or Spark-Assisted Compression Ignition (SACI). A MATLAB routine was created to predict the performance of SACI multimode combustion of natural gas using a two-zone thermodynamic model. This work performs sensitivity analysis for five performance parameters: thermal efficiency (Nth), indicated mean effective pressure (IMEP), NOx emissions, mean in-cylinder temperature (Tavg), and auto-ignition timing (AIT), with several variables such as engine speed (RPM), fuel-air equivalence ratio (0s), spark timing (0s), compression ratio (rc), and intake pressure (Pint), using the design of experiments tools to assess the factors impact. The Central Composite Design indicates that RPM and 0 were the most important SACI factors since they influence all engine performance parameters. The Pint was significant in three performance parameters (Nth, IMEP and Tavg), as was s (NOx, Tavg and AIT). The rc, however, was relevant in only one of them (AIT). Furthermore, a Univariate Analysis (UA) was done to compare Spark-Ignition (SI) and SACI engines. The results show that SACI engines tend to be around 9% more efficient, NOx emissions drop notably, more than 90%, IMEP presents an increase of 76%, and Tavg decreases 200-300 K.