Aiming at the low energy efficiency and large profile error of helical surface machining, the optimization research of milling process parameters is carried out considering the high precision and low consumption of helical surface. First, an energy consumption mechanism model for helical surface machining is established based on the energy flow and dissipation mechanisms of machine tools. Then, to achieve high-precision and low-energy-consumption machining of helical surfaces, an optimization model for helical surface milling process parameters is established. This model targets machining energy consumption and profile error values, constrained by machine tool performance and machining economy. The improved multi-objective black-winged kite algorithm (IPMOBKA) is employed to solve the problem of determining optimal process parameters for helical surfaces under conditions of multiple objectives, multiple conflicts, multiple constraints, and high dynamicity. Second, using the machining of a five-head screw rotor on the LXK300X as an example, the uniform test method is employed to collect the baseline operational power consumption of the helical groove CNC milling machine, thereby validating the machining power consumption mechanism model. Using an orthogonal experimental design, machining energy consumption, profile error, and surface roughness during helical surface machining are collected to establish predictive models for all three parameters. Finally, the optimal combination of process parameters balancing machining energy consumption and profile error is determined using a black-winged kite algorithm enhanced with a fusion of multiple mechanisms. Case verification demonstrates that the optimization method proposed in this paper reduces machining energy consumption by 15.95% while decreasing profile error by 13.22%, validating its effectiveness and providing robust support for achieving high-precision and low-energy machining of helical surfaces.